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You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:------------|
| Alexandrite |
| Opal |
| Diamond |
| Amethyst |
| Zircon |
| Pearl |
| Garnet |
| Aquamarine |
| Sapphire |
| Emerald |
| Tanzanite |
| Citrine |
| Ruby |
| Topaz |
| Turquoise |
| Peridot |
| Tourmaline |
Input Table 2:
| Col_1 |
|:----------|
| June |
| October |
| December |
| May |
| September |
| March |
| July |
| January |
| February |
| April |
| August |
| November |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case11", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case11", "case": "Case11", "label": [["Garnet", "January"], ["Amethyst", "February"], ["Aquamarine", "March"], ["Diamond", "April"], ["Emerald", "May"], ["Pearl", "June"], ["Alexandrite", "June"], ["Ruby", "July"], ["Peridot", "August"], ["Sapphire", "September"], ["Tourmaline", "October"], ["Opal", "October"], ["Topaz", "November"], ["Citrine", "November"], ["Tanzanite", "December"], ["Zircon", "December"], ["Turquoise", "December"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case11_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case11_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:--------------------|
| Larry Page |
| David Koch |
| Amancio Ortega |
| Charles Koch |
| Carlos Slim Helu |
| Warren Buffett |
| Jeff Bezos |
| Stefan Persson |
| Larry Ellison |
| Li Ka-shing |
| Jim Walton |
| Christy Walton |
| S. Robson Walton |
| Michael Bloomberg |
| Liliane Bettencourt |
| Sheldon Adelson |
| Alice Walton |
| Bernard Arnault |
| Bill Gates |
| Sergey Brin |
Input Table 2:
| Col_1 |
|:--------------|
| Sweden |
| Hong Kong |
| United States |
| Mexico |
| France |
| Spain |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case36", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case36", "case": "Case36", "label": [["Bill Gates", "United States"], ["Carlos Slim Helu", "Mexico"], ["Amancio Ortega", "Spain"], ["Warren Buffett", "United States"], ["Larry Ellison", "United States"], ["Charles Koch", "United States"], ["David Koch", "United States"], ["Sheldon Adelson", "United States"], ["Christy Walton", "United States"], ["Jim Walton", "United States"], ["Liliane Bettencourt", "France"], ["Stefan Persson", "Sweden"], ["Alice Walton", "United States"], ["S. Robson Walton", "United States"], ["Bernard Arnault", "France"], ["Michael Bloomberg", "United States"], ["Larry Page", "United States"], ["Jeff Bezos", "United States"], ["Sergey Brin", "United States"], ["Li Ka-shing", "Hong Kong"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case36_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case36_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:-------------------------------------|
| State Grid Corporation of China |
| Walmart |
| Royal Dutch Shell |
| Volkswagen |
| Sinopec |
| Toyota |
| Glencore |
| ExxonMobil |
| China National Petroleum Corporation |
| BP |
Input Table 2:
| Col_1 |
|:------------|
| Retail |
| Commodities |
| Automobiles |
| Petroleum |
| Power |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case17", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case17", "case": "Case17", "label": [["Walmart", "Retail"], ["Royal Dutch Shell", "Petroleum"], ["Sinopec", "Petroleum"], ["China National Petroleum Corporation", "Petroleum"], ["ExxonMobil", "Petroleum"], ["BP", "Petroleum"], ["State Grid Corporation of China", "Power"], ["Volkswagen", "Automobiles"], ["Toyota", "Automobiles"], ["Glencore", "Commodities"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case17_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case17_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:--------------------|
| Sony |
| Google |
| Dell |
| Toshiba |
| HP |
| Hitachi |
| Apple Inc. |
| Intel |
| Samsung Electronics |
| LG Electronics |
| IBM |
| Amazon.com |
| Panasonic |
| Microsoft |
| Foxconn |
Input Table 2:
| Col_1 |
|:-----------------------|
| Mountain View, CA, USA |
| Palo Alto, CA, USA |
| Tokyo, Japan |
| Osaka, Japan |
| Redmond, WA, USA |
| Seoul, South Korea |
| Austin, TX, USA |
| Santa Clara, CA, USA |
| New Taipei, Taiwan |
| Armonk, NY, USA |
| Cupertino, CA, USA |
| Seattle, WA, USA |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case18", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case18", "case": "Case18", "label": [["Samsung Electronics", "Seoul, South Korea"], ["Apple Inc.", "Cupertino, CA, USA"], ["Foxconn", "New Taipei, Taiwan"], ["HP", "Palo Alto, CA, USA"], ["IBM", "Armonk, NY, USA"], ["Hitachi", "Tokyo, Japan"], ["Microsoft", "Redmond, WA, USA"], ["Amazon.com", "Seattle, WA, USA"], ["Sony", "Tokyo, Japan"], ["Panasonic", "Osaka, Japan"], ["Google", "Mountain View, CA, USA"], ["Dell", "Austin, TX, USA"], ["Toshiba", "Tokyo, Japan"], ["LG Electronics", "Seoul, South Korea"], ["Intel", "Santa Clara, CA, USA"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case18_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case18_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:-------------------------------------|
| State Grid Corporation of China |
| Walmart |
| Royal Dutch Shell |
| Volkswagen |
| Sinopec |
| Toyota |
| Glencore |
| ExxonMobil |
| China National Petroleum Corporation |
| BP |
Input Table 2:
| Col_1 |
|:---------------|
| Netherlands |
| United States |
| China |
| Japan |
| United Kingdom |
| Switzerland |
| Germany |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case16", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case16", "case": "Case16", "label": [["Walmart", "United States"], ["Royal Dutch Shell", "Netherlands"], ["Sinopec", "China"], ["China National Petroleum Corporation", "China"], ["ExxonMobil", "United States"], ["BP", "United Kingdom"], ["State Grid Corporation of China", "China"], ["Volkswagen", "Germany"], ["Toyota", "Japan"], ["Glencore", "Switzerland"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case16_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case16_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:-----------------------------------|
| Michael Jackson's This Is It |
| All the King's Men |
| To Sir, with Love |
| Guess Who's Coming to Dinner |
| Taxi Driver |
| Close Encounters of the Third Kind |
| Ghostbusters |
| The Karate Kid |
| Spider-Man |
| Men in Black |
| Bad Boys |
| James Bond series |
| The Ten Commandments |
| Vertigo |
| Rosemary's Baby |
| The Odd Couple |
| The Godfather |
| The Bad News Bears |
| Saturday Night Fever |
| Beverly Hills Cop |
| Star Trek |
| Top Gun |
| Ghost |
| Braveheart |
| Mission: Impossible |
| Indiana Jones |
| Forrest Gump |
| Saving Private Ryan |
| Transformers |
| Friday The 13th |
| Noah |
| Bright Eyes |
| Son of Fury |
| Home in Indiana |
| Journey to the Center of the Earth |
| The Sound of Music |
| Fantastic Voyage |
| Star Wars |
| X-Men |
| Die Hard |
| Alien |
| Planet of the Apes |
| Home Alone |
| Predator |
| Speed |
| Independence Day |
| Avatar |
| The Simpsons Movie |
| The Wolf Man |
| The Mummy |
| Spartacus |
| Airport |
| Jaws |
| The Blues Brothers |
| The Deer Hunter |
| Rollercoaster |
| Back to the Future |
| Field of Dreams |
| Backdraft |
| Beethoven |
| Dr. Giggles |
| Jurassic Park |
| Meet the Parents |
| The Bourne Identity |
| The Sting |
| E.T. |
| Schindler's List |
| Dracula |
| Oblivion |
| Rush |
Input Table 2:
| Col_1 |
|:-------------------|
| Columbia Pictures |
| Eon Productions |
| Paramount Pictures |
| 20th Century Fox |
| Universal Studios |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case22", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case22", "case": "Case22", "label": [["Michael Jackson's This Is It", "Columbia Pictures"], ["All the King's Men", "Columbia Pictures"], ["To Sir, with Love", "Columbia Pictures"], ["Guess Who's Coming to Dinner", "Columbia Pictures"], ["Taxi Driver", "Columbia Pictures"], ["Close Encounters of the Third Kind", "Columbia Pictures"], ["Ghostbusters", "Columbia Pictures"], ["The Karate Kid", "Columbia Pictures"], ["Spider-Man", "Columbia Pictures"], ["Men in Black", "Columbia Pictures"], ["Bad Boys", "Columbia Pictures"], ["James Bond series", "Eon Productions"], ["The Ten Commandments", "Paramount Pictures"], ["Vertigo", "Paramount Pictures"], ["Rosemary's Baby", "Paramount Pictures"], ["The Odd Couple", "Paramount Pictures"], ["The Godfather", "Paramount Pictures"], ["The Bad News Bears", "Paramount Pictures"], ["Saturday Night Fever", "Paramount Pictures"], ["Beverly Hills Cop", "Paramount Pictures"], ["Star Trek", "Paramount Pictures"], ["Top Gun", "Paramount Pictures"], ["Ghost", "Paramount Pictures"], ["Braveheart", "Paramount Pictures"], ["Mission: Impossible", "Paramount Pictures"], ["Indiana Jones", "Paramount Pictures"], ["Forrest Gump", "Paramount Pictures"], ["Saving Private Ryan", "Paramount Pictures"], ["Transformers", "Paramount Pictures"], ["Friday The 13th", "Paramount Pictures"], ["Noah", "Paramount Pictures"], ["Bright Eyes", "20th Century Fox"], ["Son of Fury", "20th Century Fox"], ["Home in Indiana", "20th Century Fox"], ["Journey to the Center of the Earth", "20th Century Fox"], ["The Sound of Music", "20th Century Fox"], ["Fantastic Voyage", "20th Century Fox"], ["Star Wars", "20th Century Fox"], ["X-Men", "20th Century Fox"], ["Die Hard", "20th Century Fox"], ["Alien", "20th Century Fox"], ["Planet of the Apes", "20th Century Fox"], ["Home Alone", "20th Century Fox"], ["Predator", "20th Century Fox"], ["Speed", "20th Century Fox"], ["Independence Day", "20th Century Fox"], ["Avatar", "20th Century Fox"], ["The Simpsons Movie", "20th Century Fox"], ["The Wolf Man", "Universal Studios"], ["The Mummy", "Universal Studios"], ["Spartacus", "Universal Studios"], ["Airport", "Universal Studios"], ["Jaws", "Universal Studios"], ["The Blues Brothers", "Universal Studios"], ["The Deer Hunter", "Universal Studios"], ["Rollercoaster", "Universal Studios"], ["Back to the Future", "Universal Studios"], ["Field of Dreams", "Universal Studios"], ["Backdraft", "Universal Studios"], ["Beethoven", "Universal Studios"], ["Dr. Giggles", "Universal Studios"], ["Jurassic Park", "Universal Studios"], ["Meet the Parents", "Universal Studios"], ["The Bourne Identity", "Universal Studios"], ["The Sting", "Universal Studios"], ["E.T.", "Universal Studios"], ["Schindler's List", "Universal Studios"], ["Dracula", "Universal Studios"], ["Oblivion", "Universal Studios"], ["Rush", "Universal Studios"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case22_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case22_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:-----------|
| Parnate |
| Nardil |
| Marplan |
| Anafranil |
| Elavil |
| Norpramin |
| Pamelor |
| Sinequan |
| Surmontil |
| Celexa |
| Lexapro |
| Luvox |
| Paxil |
| Prozac |
| Zoloft |
| Cymbalta |
| Effexor |
| Remeron |
| Wellbutrin |
| Desyrel |
Input Table 2:
| Col_1 |
|:----------------|
| tranylcypromine |
| phenelzine |
| isocarboxazid |
| clomipramine |
| amitriptyline |
| desipramine |
| nortriptyline |
| doxepin |
| trimipramine |
| citalopram |
| escitalopram |
| fluvoxamine |
| paroxetine |
| fluoxetine |
| sertraline |
| duloxetine |
| venlafaxine |
| mirtazapine |
| bupropion |
| trazodone |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case2", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case2", "case": "Case2", "label": [["Parnate", "tranylcypromine"], ["Nardil", "phenelzine"], ["Marplan", "isocarboxazid"], ["Anafranil", "clomipramine"], ["Elavil", "amitriptyline"], ["Norpramin", "desipramine"], ["Pamelor", "nortriptyline"], ["Sinequan", "doxepin"], ["Surmontil", "trimipramine"], ["Celexa", "citalopram"], ["Lexapro", "escitalopram"], ["Luvox", "fluvoxamine"], ["Paxil", "paroxetine"], ["Prozac", "fluoxetine"], ["Zoloft", "sertraline"], ["Cymbalta", "duloxetine"], ["Effexor", "venlafaxine"], ["Remeron", "mirtazapine"], ["Wellbutrin", "bupropion"], ["Desyrel", "trazodone"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case2_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case2_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:----------------------------------------|
| Mario Kart 7 |
| New Super Mario Bros. 2 |
| Monster Hunter 3 Ultimate |
| Kid Icarus: Uprising |
| Dragon Warrior VII |
| The Legend of Zelda: Ocarina of Time 3D |
| Nintendogs + Cats |
| Luigi's Mansion: Dark Moon |
| Super Mario 3D Land |
| Mario Tennis Open |
| Animal Crossing: New Leaf |
| Paper Mario: Sticker Star |
| Super Street Fighter IV: 3D Edition |
Input Table 2:
| Col_1 |
|:------------|
| Nintendo |
| Square Enix |
| Capcom |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case27", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case27", "case": "Case27", "label": [["Super Mario 3D Land", "Nintendo"], ["Mario Kart 7", "Nintendo"], ["New Super Mario Bros. 2", "Nintendo"], ["Animal Crossing: New Leaf", "Nintendo"], ["Nintendogs + Cats", "Nintendo"], ["The Legend of Zelda: Ocarina of Time 3D", "Nintendo"], ["Paper Mario: Sticker Star", "Nintendo"], ["Monster Hunter 3 Ultimate", "Capcom"], ["Luigi's Mansion: Dark Moon", "Nintendo"], ["Dragon Warrior VII", "Square Enix"], ["Kid Icarus: Uprising", "Nintendo"], ["Mario Tennis Open", "Nintendo"], ["Super Street Fighter IV: 3D Edition", "Capcom"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case27_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case27_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:--------------------------------------------------|
| Saving Private Ryan |
| Schindler's List |
| City of God |
| One Flew Over the Cuckoo's Nest |
| The Dark Knight |
| The Silence of the Lambs |
| Casablanca |
| 12 Angry Men |
| Life Is Beautiful |
| The Lord of the Rings: The Return of the King |
| The Godfather |
| The Godfather: Part II |
| The Lord of the Rings: The Two Towers |
| Raiders of the Lost Ark |
| Pulp Fiction |
| Fight Club |
| Once Upon a Time in the West |
| American History X |
| Goodfellas |
| The Usual Suspects |
| Psycho |
| Star Wars: Episode IV - A New Hope |
| The Matrix |
| It's a Wonderful Life |
| Se7en |
| Forrest Gump |
| The Lord of the Rings: The Fellowship of the Ring |
| Léon: The Professional |
| Seven Samurai |
| Star Wars: Episode V - The Empire Strikes Back |
| The Good, the Bad and the Ugly |
| Inception |
Input Table 2:
| Col_1 |
|--------:|
| 2010 |
| 1954 |
| 1998 |
| 1975 |
| 1997 |
| 2008 |
| 1974 |
| 1995 |
| 1972 |
| 1994 |
| 1993 |
| 2003 |
| 2002 |
| 1991 |
| 1957 |
| 1999 |
| 1977 |
| 2001 |
| 1990 |
| 1942 |
| 1960 |
| 1981 |
| 1980 |
| 1946 |
| 1968 |
| 1966 |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case21", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case21", "case": "Case21", "label": [["The Godfather", 1972], ["Fight Club", 1999], ["The Lord of the Rings: The Fellowship of the Ring", 2001], ["Star Wars: Episode V - The Empire Strikes Back", 1980], ["Inception", 2010], ["Forrest Gump", 1994], ["One Flew Over the Cuckoo's Nest", 1975], ["The Lord of the Rings: The Two Towers", 2002], ["Goodfellas", 1990], ["The Matrix", 1999], ["Star Wars: Episode IV - A New Hope", 1977], ["Seven Samurai", 1954], ["City of God", 2002], ["Se7en", 1995], ["The Usual Suspects", 1995], ["The Silence of the Lambs", 1991], ["It's a Wonderful Life", 1946], ["Once Upon a Time in the West", 1968], ["L\u00e9on: The Professional", 1994], ["Life Is Beautiful", 1997], ["Casablanca", 1942], ["The Godfather: Part II", 1974], ["Raiders of the Lost Ark", 1981], ["American History X", 1998], ["Psycho", 1960], ["Saving Private Ryan", 1998], ["The Dark Knight", 2008], ["Pulp Fiction", 1994], ["The Good, the Bad and the Ugly", 1966], ["Schindler's List", 1993], ["12 Angry Men", 1957], ["The Lord of the Rings: The Return of the King", 2003]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case21_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case21_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:-----------------------|
| Memphis Grizzlies |
| Los Angeles Clippers |
| San Antonio Spurs |
| Boston Celtics |
| Utah Jazz |
| Milwaukee Bucks |
| Charlotte Bobcats |
| Los Angeles Lakers |
| Sacramento Kings |
| Indiana Pacers |
| Oklahoma City Thunder |
| Philadelphia 76ers |
| Portland Trail Blazers |
| Miami Heat |
| Brooklyn Nets |
| New York Knicks |
| Golden State Warriors |
| Minnesota Timberwolves |
| Toronto Raptors |
| Atlanta Hawks |
| Houston Rockets |
| Detroit Pistons |
| Dallas Mavericks |
| Denver Nuggets |
| Cleveland Cavaliers |
| Chicago Bulls |
| New Orleans Hornets |
| Washington Wizards |
| Phoenix Suns |
| Orlando Magic |
Input Table 2:
| Col_1 |
|:----------|
| Southwest |
| Northwest |
| Pacific |
| Central |
| Southeast |
| Atlantic |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case30", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case30", "case": "Case30", "label": [["Boston Celtics", "Atlantic"], ["Brooklyn Nets", "Atlantic"], ["New York Knicks", "Atlantic"], ["Philadelphia 76ers", "Atlantic"], ["Toronto Raptors", "Atlantic"], ["Chicago Bulls", "Central"], ["Cleveland Cavaliers", "Central"], ["Detroit Pistons", "Central"], ["Indiana Pacers", "Central"], ["Milwaukee Bucks", "Central"], ["Atlanta Hawks", "Southeast"], ["Charlotte Bobcats", "Southeast"], ["Miami Heat", "Southeast"], ["Orlando Magic", "Southeast"], ["Washington Wizards", "Southeast"], ["Dallas Mavericks", "Southwest"], ["Houston Rockets", "Southwest"], ["Memphis Grizzlies", "Southwest"], ["New Orleans Hornets", "Southwest"], ["San Antonio Spurs", "Southwest"], ["Denver Nuggets", "Northwest"], ["Minnesota Timberwolves", "Northwest"], ["Portland Trail Blazers", "Northwest"], ["Oklahoma City Thunder", "Northwest"], ["Utah Jazz", "Northwest"], ["Golden State Warriors", "Pacific"], ["Los Angeles Clippers", "Pacific"], ["Los Angeles Lakers", "Pacific"], ["Phoenix Suns", "Pacific"], ["Sacramento Kings", "Pacific"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case30_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case30_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:-----------------------|
| Memphis Grizzlies |
| Los Angeles Clippers |
| San Antonio Spurs |
| Boston Celtics |
| Utah Jazz |
| Milwaukee Bucks |
| Charlotte Bobcats |
| Los Angeles Lakers |
| Sacramento Kings |
| Indiana Pacers |
| Oklahoma City Thunder |
| Philadelphia 76ers |
| Portland Trail Blazers |
| Miami Heat |
| Brooklyn Nets |
| New York Knicks |
| Golden State Warriors |
| Minnesota Timberwolves |
| Toronto Raptors |
| Atlanta Hawks |
| Houston Rockets |
| Detroit Pistons |
| Dallas Mavericks |
| Denver Nuggets |
| Cleveland Cavaliers |
| Chicago Bulls |
| New Orleans Hornets |
| Washington Wizards |
| Phoenix Suns |
| Orlando Magic |
Input Table 2:
| Col_1 |
|:---------------------------|
| AT&T Center |
| Barclays Center |
| American Airlines Center |
| US Airways Center |
| TD Garden |
| Amway Center |
| Oracle Arena |
| The Palace of Auburn Hills |
| Sleep Train Arena |
| Verizon Center |
| United Center |
| Staples Center |
| Bankers Life Fieldhouse |
| Rose Garden |
| Time Warner Cable Arena |
| Philips Arena |
| New Orleans Arena |
| EnergySolutions Arena |
| Target Center |
| Quicken Loans Arena |
| Toyota Center |
| Madison Square Garden |
| FedExForum |
| Chesapeake Energy Arena |
| Air Canada Center |
| American Airlines Arena |
| Pepsi Center |
| BMO Harris Bradley Center |
| Wells Fargo Center |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case31", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case31", "case": "Case31", "label": [["Boston Celtics", "TD Garden"], ["Brooklyn Nets", "Barclays Center"], ["New York Knicks", "Madison Square Garden"], ["Philadelphia 76ers", "Wells Fargo Center"], ["Toronto Raptors", "Air Canada Center"], ["Chicago Bulls", "United Center"], ["Cleveland Cavaliers", "Quicken Loans Arena"], ["Detroit Pistons", "The Palace of Auburn Hills"], ["Indiana Pacers", "Bankers Life Fieldhouse"], ["Milwaukee Bucks", "BMO Harris Bradley Center"], ["Atlanta Hawks", "Philips Arena"], ["Charlotte Bobcats", "Time Warner Cable Arena"], ["Miami Heat", "American Airlines Arena"], ["Orlando Magic", "Amway Center"], ["Washington Wizards", "Verizon Center"], ["Dallas Mavericks", "American Airlines Center"], ["Houston Rockets", "Toyota Center"], ["Memphis Grizzlies", "FedExForum"], ["New Orleans Hornets", "New Orleans Arena"], ["San Antonio Spurs", "AT&T Center"], ["Denver Nuggets", "Pepsi Center"], ["Minnesota Timberwolves", "Target Center"], ["Portland Trail Blazers", "Rose Garden"], ["Oklahoma City Thunder", "Chesapeake Energy Arena"], ["Utah Jazz", "EnergySolutions Arena"], ["Golden State Warriors", "Oracle Arena"], ["Los Angeles Clippers", "Staples Center"], ["Los Angeles Lakers", "Staples Center"], ["Phoenix Suns", "US Airways Center"], ["Sacramento Kings", "Sleep Train Arena"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case31_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case31_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:---------------|
| Benin |
| Paraguay |
| Portugal |
| Syria |
| Bahamas |
| Greece |
| Mongolia |
| Morocco |
| Guatemala |
| Guyana |
| Czechoslovakia |
| Iraq |
| Chile |
| Nepal |
| Argentina |
| Seychelles |
| Tanzania |
| Belize |
| Ghana |
| Zambia |
| Bahrain |
| Congo |
| India |
| Canada |
| Maldives |
| Turkey |
| Belgium |
| Finland |
| Taiwan |
| Jamaica |
| Peru |
| Germany |
| Fiji |
| Guinea |
| Chad |
| Somalia |
| Thailand |
| Sweden |
| Vietnam |
| Malawi |
| Andorra |
| Poland |
| Bulgaria |
| Jordan |
| Kuwait |
| Tunisia |
| Uruguay |
| Kenya |
| Switzerland |
| Spain |
| Brunei |
| Djibouti |
| Lebanon |
| Cuba |
| Venezuela |
| Mauritania |
| Israel |
| Australia |
| Cameroon |
| Malaysia |
| Iceland |
| Oman |
| Gabon |
| Austria |
| Luxembourg |
| Brazil |
| Algeria |
| Slovenia |
| Ecuador |
| Hungary |
| Japan |
| Mauritius |
| Columbia |
| Senegal |
| Honduras |
| Italy |
| Ethiopia |
| Haiti |
| Afghanistan |
| Singapore |
| Egypt |
| Bolivia |
| Malta |
| Netherlands |
| Pakistan |
| Gambia |
| China |
| Ireland |
| Qatar |
| France |
| Serbia |
| Romania |
| Togo |
| Niger |
| Philippines |
| Barbados |
| Nicaragua |
| Norway |
| Denmark |
| Mexico |
| Uganda |
| Zimbabwe |
| Suriname |
| Montenegro |
| Indonesia |
Input Table 2:
| Col_1 |
|:------------|
| ouguiya |
| euro |
| krona |
| shekel |
| krone |
| dirham |
| dinar |
| guarani |
| cedi |
| rupiah |
| dollar |
| gourde |
| rupee |
| pound |
| lempira |
| ringgit |
| kwacha |
| boliviano |
| franc |
| forint |
| lev |
| tugrik |
| leu |
| lira |
| yen |
| guilder |
| peso |
| baht |
| riyal-omani |
| dalasi |
| birr |
| real |
| inti |
| zloty |
| dong |
| Krona |
| bolivar |
| shilling |
| rufiyaa |
| afghani |
| yuan |
| koruna |
| riyal |
| cordoba |
| quetzal |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case23", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case23", "case": "Case23", "label": [["Mauritania", "ouguiya"], ["China", "yuan"], ["Afghanistan", "afghani"], ["Algeria", "dinar"], ["Andorra", "euro"], ["Argentina", "peso"], ["Australia", "dollar"], ["Austria", "euro"], ["Bahamas", "dollar"], ["Barbados", "dollar"], ["Belgium", "euro"], ["Belize", "dollar"], ["Benin", "franc"], ["Bolivia", "boliviano"], ["Brazil", "real"], ["Brunei", "dollar"], ["Bulgaria", "lev"], ["Cameroon", "franc"], ["Canada", "dollar"], ["Chad", "franc"], ["Chile", "peso"], ["Columbia", "peso"], ["Congo", "franc"], ["Cuba", "peso"], ["Czechoslovakia", "koruna"], ["Denmark", "krone"], ["Djibouti", "franc"], ["Ecuador", "dollar"], ["Egypt", "pound"], ["Ethiopia", "birr"], ["Fiji", "dollar"], ["Finland", "euro"], ["France", "euro"], ["Gabon", "franc"], ["Gambia", "dalasi"], ["Germany", "euro"], ["Ghana", "cedi"], ["Greece", "euro"], ["Guatemala", "quetzal"], ["Guinea", "franc"], ["Guyana", "dollar"], ["Haiti", "gourde"], ["Honduras", "lempira"], ["Hungary", "forint"], ["Iceland", "Krona"], ["India", "rupee"], ["Indonesia", "rupiah"], ["Ireland", "euro"], ["Israel", "shekel"], ["Italy", "euro"], ["Jamaica", "dollar"], ["Japan", "yen"], ["Kenya", "shilling"], ["Lebanon", "pound"], ["Luxembourg", "euro"], ["Malawi", "kwacha"], ["Malaysia", "ringgit"], ["Maldives", "rufiyaa"], ["Malta", "euro"], ["Mauritius", "rupee"], ["Mexico", "peso"], ["Mongolia", "tugrik"], ["Montenegro", "euro"], ["Morocco", "dirham"], ["Nepal", "rupee"], ["Netherlands", "euro"], ["Nicaragua", "cordoba"], ["Niger", "franc"], ["Norway", "krone"], ["Pakistan", "rupee"], ["Paraguay", "guarani"], ["Peru", "inti"], ["Philippines", "peso"], ["Poland", "zloty"], ["Portugal", "euro"], ["Qatar", "riyal"], ["Romania", "leu"], ["Senegal", "franc"], ["Serbia", "dinar"], ["Seychelles", "rupee"], ["Singapore", "dollar"], ["Slovenia", "euro"], ["Somalia", "shilling"], ["Spain", "euro"], ["Suriname", "guilder"], ["Sweden", "krona"], ["Switzerland", "franc"], ["Syria", "pound"], ["Taiwan", "dollar"], ["Tanzania", "shilling"], ["Thailand", "baht"], ["Togo", "franc"], ["Turkey", "lira"], ["Uganda", "shilling"], ["Uruguay", "peso"], ["Venezuela", "bolivar"], ["Vietnam", "dong"], ["Zambia", "kwacha"], ["Zimbabwe", "dollar"], ["Bahrain", "dinar"], ["Iraq", "dinar"], ["Jordan", "dinar"], ["Kuwait", "dinar"], ["Oman", "riyal-omani"], ["Tunisia", "dinar"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case23_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case23_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:------------|
| Afghanistan |
| Bhutan |
| Singapore |
| Sri Lanka |
| Sikkim |
| England |
| Malaysia |
| India |
| Bangladesh |
| Maldives |
| Iran |
| Kashmir |
| China |
| Nepal |
Input Table 2:
| Col_1 |
|:-----------------|
| Tudor rose |
| Shapla |
| Orchid |
| Blue poppy |
| Pink Rose |
| Peony |
| Tulip |
| Chinese hibiscus |
| Noble orchid |
| Water lily |
| Lotus |
| Rhododendron |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case42", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case42", "case": "Case42", "label": [["England", "Tudor rose"], ["Nepal", "Rhododendron"], ["Sikkim", "Noble orchid"], ["Sri Lanka", "Water lily"], ["Afghanistan", "Tulip"], ["Bangladesh", "Shapla"], ["Bhutan", "Blue poppy"], ["China", "Peony"], ["India", "Lotus"], ["Iran", "Tulip"], ["Kashmir", "Rhododendron"], ["Maldives", "Pink Rose"], ["Malaysia", "Chinese hibiscus"], ["Singapore", "Orchid"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case42_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case42_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:-----------------------------|
| FREEPORT-MCMORAN COP&GOLD |
| HEALTH CARE REIT INC |
| EATON CORP PLC |
| TIME WARNER INC |
| BOEING CO |
| GAMCO INVESTORS INC |
| VERIFONE SYSTEMS INC |
| PFIZER INC |
| HAIN CELESTIAL GROUP INC |
| DISCOVERY COMMUNICATIONS INC |
| TRW AUTOMOTIVE HOLDINGS CORP |
| SIRIUS XM HOLDINGS INC |
| HONEYWELL INTERNATIONAL INC |
| SCHLUMBERGER LTD |
| AMERICAN EXPRESS CO |
| LINKEDIN CORP |
| SOLERA HOLDINGS INC |
| GENERAL ELECTRIC CO |
| QUALCOMM INC |
| CUMULUS MEDIA INC |
| TJX COMPANIES INC |
| DISNEY (WALT) CO |
| EXXON MOBIL CORP |
| WYNN RESORTS LTD |
| AT&T INC |
| MARATHON OIL CORP |
| AMERIPRISE FINANCIAL INC |
| CHEVRON CORP |
| TRIPADVISOR INC |
| FORD MOTOR CO |
| YAHOO INC |
| T-MOBILE US INC |
| ABBOTT LABORATORIES |
| HALLIBURTON CO |
| UNITED TECHNOLOGIES CORP |
| SLM CORP -SPN |
| PANDORA MEDIA INC |
| SPECTRUM BRANDS HOLDINGS INC |
| TENET HEALTHCARE CORP |
| VIACOM INC |
| GAP INC |
| SPRINT CORP |
| NABORS INDUSTRIES LTD |
| NUANCE COMMUNICATIONS INC |
| ORACLE CORP |
| ZYNGA INC |
| CISCO SYSTEMS INC |
| CVS CAREMARK CORP |
| WELLS FARGO & CO |
| DISCOVER FINANCIAL SVCS INC |
| JARDEN CORP |
| NIELSEN HOLDINGS NV |
| MDC PARTNERS INC |
| CELGENE CORP |
| UNITED THERAPEUTICS CORP |
| COCA-COLA CO |
| DOW CHEMICAL |
| MCKESSON CORP |
| MOBILE MINI INC |
| COMPUTER SCIENCES CORP |
| ALTRIA GROUP INC |
| RALPH LAUREN CORP |
| REGENERON PHARMACEUTICALS |
| BRISTOL-MYERS SQUIBB CO |
| COMCAST CORP |
| CBS CORP |
| LOWE'S COMPANIES INC |
| CONOCOPHILLIPS |
| AFFILIATED MANAGERS GRP INC |
| AVAGO TECHNOLOGIES LTD |
| ROPER INDUSTRIES INC |
| VISA INC |
| PHILLIPS 66 |
| LAUDER (ESTEE) COS INC |
| DANAHER CORP |
| CHIPOTLE MEXICAN GRILL INC |
| GENERAL DYNAMICS CORP |
| TRANSDIGM GROUP INC |
| HASBRO INC |
| HELEN OF TROY LTD |
| FLEETCOR TECHNOLOGIES INC |
| AETNA INC |
| CHENIERE ENERGY INC |
| SANDRIDGE ENERGY INC |
| SALESFORCE.COM INC |
| PARKER-HANNIFIN CORP |
| EMERSON ELECTRIC CO |
| REALOGY HOLDINGS CORP |
| CHESAPEAKE ENERGY CORP |
| ENDURANCE SPECIALTY HOLDINGS |
| GOODYEAR TIRE & RUBBER CO |
| OFFICE DEPOT INC |
| RESTORATION HARDWARE HLDNGS |
| STARZ |
| LOCKHEED MARTIN CORP |
| GOLDMAN SACHS GROUP INC |
| EVEREST RE GROUP LTD |
| DEERE & CO |
| BOSTON PROPERTIES INC |
| GENERAL GROWTH PPTYS INC |
Input Table 2:
| Col_1 |
|:---------------------------------|
| Jeffrey Weiner |
| Charles W. Scharf |
| Leslie Moonves |
| J. S. Watson |
| Greg C. Garland |
| Clarence P. Cazalot Jr. |
| Hock E. Tan |
| Brian McAndrews |
| David W. Nelms |
| Erik Olsson |
| Mario J. Gabelli |
| Don A. Mattrick |
| David J. Lesar |
| Richard C. Adkerson |
| James E. Meyer |
| George L. Chapman |
| Phebe N. Novakovic |
| Brian L. Roberts |
| Paul E. Jacobs |
| Stephen Kaufer |
| Paul A. Ricci |
| Ralph Lauren |
| Marillyn A. Hewson |
| Muhtar Kent |
| Fabrizio Freda |
| Gerald J. Rubin |
| Samuel R. Allen |
| Miles S. Nadal |
| Robert A. Niblock |
| Donald E. Washkewicz |
| W. James McNerney Jr. |
| Miles D. White |
| Philippe P. Dauman |
| Gary Friedman |
| Richard A. Smith |
| John H. Hammergren |
| R. W. Tillerson |
| Glenn Murphy |
| R. Stephenson |
| W. Nicholas Howley |
| Marc Benioff |
| Lloyd C. Blankfein |
| Ronald F. Clarke |
| David M. Cote |
| D. N. Farr |
| Trevor Fetter |
| Tom L. Ward |
| Carol Meyrowitz |
| Charif Souki |
| Jeffrey R. Immelt |
| Jeffrey L. Bewkes |
| Martine A. Rothblatt |
| Mortimer B. Zuckerman |
| H. Lawrence Culp Jr. |
| Daniel R. Hesse |
| Leonard S. Schleifer M.D., Ph.D. |
| Louis Ch?nevert |
| Roland Smith |
| Sandeep Mathrani |
| Marissa A. Mayer |
| Irwin D. Simon |
| John J. Legere |
| Brian Goldner |
| John T. Chambers |
| J. Michael Lawrie |
| Lamberto Andreotti |
| Stephen A. Wynn |
| Anthony G. Petrello |
| Paul Galant |
| Andrew N. Liveris |
| John G. Stumpf |
| James E. Lillie |
| David Calhoun |
| Sean M. Healey |
| K. I. Chenault |
| Robert D. (?Doug?) Lawler |
| Richard J. Kramer |
| James M. Cracchiolo |
| David M. Zaslav |
| Brian D. Jellison |
| Tony Aquila |
| Alan Mulally |
| A. M. Cutler |
| Lewis W. Dickey Jr. |
| John R. Charman |
| Mark Bertolini |
| R. M. Lance |
| Joseph V. Taranto |
| Robert J. Hugin |
| Steve Ells |
| David R. Lumley |
| P. Kibsgaard |
| John C. Plant |
| Robert A. Iger |
| Albert L. Lord |
| Larry J. Merlo |
| Christopher P. Albrecht |
| Martin J. Barrington |
| Lawrence J. Ellison |
| I. Read |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case19", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case19", "case": "Case19", "label": [["CHENIERE ENERGY INC", "Charif Souki"], ["GAMCO INVESTORS INC", "Mario J. Gabelli"], ["ORACLE CORP", "Lawrence J. Ellison"], ["SANDRIDGE ENERGY INC", "Tom L. Ward"], ["NABORS INDUSTRIES LTD", "Anthony G. Petrello"], ["CBS CORP", "Leslie Moonves"], ["TRANSDIGM GROUP INC", "W. Nicholas Howley"], ["ZYNGA INC", "Don A. Mattrick"], ["FREEPORT-MCMORAN COP&GOLD", "Richard C. Adkerson"], ["MCKESSON CORP", "John H. Hammergren"], ["SPRINT CORP", "Daniel R. Hesse"], ["LINKEDIN CORP", "Jeffrey Weiner"], ["ENDURANCE SPECIALTY HOLDINGS", "John R. Charman"], ["HELEN OF TROY LTD", "Gerald J. Rubin"], ["TRIPADVISOR INC", "Stephen Kaufer"], ["UNITED THERAPEUTICS CORP", "Martine A. Rothblatt"], ["VIACOM INC", "Philippe P. Dauman"], ["RESTORATION HARDWARE HLDNGS", "Gary Friedman"], ["REGENERON PHARMACEUTICALS", "Leonard S. Schleifer M.D., Ph.D."], ["DISNEY (WALT) CO", "Robert A. Iger"], ["DISCOVERY COMMUNICATIONS INC", "David M. Zaslav"], ["FLEETCOR TECHNOLOGIES INC", "Ronald F. Clarke"], ["TIME WARNER INC", "Jeffrey L. Bewkes"], ["AVAGO TECHNOLOGIES LTD", "Hock E. Tan"], ["LAUDER (ESTEE) COS INC", "Fabrizio Freda"], ["COMCAST CORP", "Brian L. Roberts"], ["SALESFORCE.COM INC", "Marc Benioff"], ["CVS CAREMARK CORP", "Larry J. Merlo"], ["AETNA INC", "Mark Bertolini"], ["STARZ", "Christopher P. Albrecht"], ["SOLERA HOLDINGS INC", "Tony Aquila"], ["T-MOBILE US INC", "John J. Legere"], ["NUANCE COMMUNICATIONS INC", "Paul A. Ricci"], ["PANDORA MEDIA INC", "Brian McAndrews"], ["EXXON MOBIL CORP", "R. W. Tillerson"], ["PARKER-HANNIFIN CORP", "Donald E. Washkewicz"], ["HASBRO INC", "Brian Goldner"], ["HAIN CELESTIAL GROUP INC", "Irwin D. Simon"], ["RALPH LAUREN CORP", "Ralph Lauren"], ["HONEYWELL INTERNATIONAL INC", "David M. Cote"], ["EMERSON ELECTRIC CO", "D. N. Farr"], ["LOCKHEED MARTIN CORP", "Marillyn A. Hewson"], ["CHIPOTLE MEXICAN GRILL INC", "Steve Ells"], ["YAHOO INC", "Marissa A. Mayer"], ["TRW AUTOMOTIVE HOLDINGS CORP", "John C. Plant"], ["VISA INC", "Charles W. Scharf"], ["MOBILE MINI INC", "Erik Olsson"], ["CHEVRON CORP", "J. S. Watson"], ["REALOGY HOLDINGS CORP", "Richard A. Smith"], ["BOSTON PROPERTIES INC", "Mortimer B. Zuckerman"], ["CONOCOPHILLIPS", "R. M. Lance"], ["MARATHON OIL CORP", "Clarence P. Cazalot Jr."], ["BOEING CO", "W. James McNerney Jr."], ["AT&T INC", "R. Stephenson"], ["FORD MOTOR CO", "Alan Mulally"], ["EATON CORP PLC", "A. M. Cutler"], ["SIRIUS XM HOLDINGS INC", "James E. Meyer"], ["SCHLUMBERGER LTD", "P. Kibsgaard"], ["TENET HEALTHCARE CORP", "Trevor Fetter"], ["TJX COMPANIES INC", "Carol Meyrowitz"], ["CHESAPEAKE ENERGY CORP", "Robert D. (?Doug?) Lawler"], ["GENERAL GROWTH PPTYS INC", "Sandeep Mathrani"], ["AMERICAN EXPRESS CO", "K. I. Chenault"], ["ROPER INDUSTRIES INC", "Brian D. Jellison"], ["COMPUTER SCIENCES CORP", "J. Michael Lawrie"], ["EVEREST RE GROUP LTD", "Joseph V. Taranto"], ["DISCOVER FINANCIAL SVCS INC", "David W. Nelms"], ["CISCO SYSTEMS INC", "John T. Chambers"], ["CELGENE CORP", "Robert J. Hugin"], ["ABBOTT LABORATORIES", "Miles D. White"], ["HALLIBURTON CO", "David J. Lesar"], ["BRISTOL-MYERS SQUIBB CO", "Lamberto Andreotti"], ["MDC PARTNERS INC", "Miles S. Nadal"], ["DOW CHEMICAL", "Andrew N. Liveris"], ["QUALCOMM INC", "Paul E. Jacobs"], ["VERIFONE SYSTEMS INC", "Paul Galant"], ["COCA-COLA CO", "Muhtar Kent"], ["UNITED TECHNOLOGIES CORP", "Louis Ch?nevert"], ["SLM CORP -SPN", "Albert L. Lord"], ["ALTRIA GROUP INC", "Martin J. Barrington"], ["AFFILIATED MANAGERS GRP INC", "Sean M. Healey"], ["CUMULUS MEDIA INC", "Lewis W. Dickey Jr."], ["GOLDMAN SACHS GROUP INC", "Lloyd C. Blankfein"], ["HEALTH CARE REIT INC", "George L. Chapman"], ["PHILLIPS 66", "Greg C. Garland"], ["GENERAL ELECTRIC CO", "Jeffrey R. Immelt"], ["AMERIPRISE FINANCIAL INC", "James M. Cracchiolo"], ["DANAHER CORP", "H. Lawrence Culp Jr."], ["OFFICE DEPOT INC", "Roland Smith"], ["WYNN RESORTS LTD", "Stephen A. Wynn"], ["NIELSEN HOLDINGS NV", "David Calhoun"], ["WELLS FARGO & CO", "John G. Stumpf"], ["GOODYEAR TIRE & RUBBER CO", "Richard J. Kramer"], ["DEERE & CO", "Samuel R. Allen"], ["JARDEN CORP", "James E. Lillie"], ["GENERAL DYNAMICS CORP", "Phebe N. Novakovic"], ["PFIZER INC", "I. Read"], ["SPECTRUM BRANDS HOLDINGS INC", "David R. Lumley"], ["GAP INC", "Glenn Murphy"], ["LOWE'S COMPANIES INC", "Robert A. Niblock"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case19_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case19_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:-------------------------------------------------|
| Denver International Airport |
| Leonardo da Vinci Airport |
| Barcelona Airport |
| O'Hare International Airport |
| Munich Airport |
| Phoenix Sky Harbor International Airport |
| John F. Kennedy International Airport |
| Minneapolis-Saint Paul International Airport |
| Atatürk International Airport |
| Dubai International Airport |
| Los Angeles International Airport |
| London Gatwick Airport |
| Toronto Pearson International Airport |
| Paris Charles de Gaulle Airport |
| Shanghai Pudong International Airport |
| Singapore Changi Airport |
| Seoul Incheon International Airport |
| London Heathrow Airport |
| Paris-Orly Airport |
| Frankfurt Airport |
| Newark Liberty International Airport |
| Charlotte Douglas International Airport |
| Dublin Airport |
| McCarran International Airport |
| Dallas-Fort Worth International Airport |
| Mexico City International Airport |
| Narita International Airport |
| Indira Gandhi International Airport |
| Tokyo International Airport |
| Madrid-Barajas Airport |
| Chhatrapati Shivaji International Airport |
| Hartsfield-Jackson Atlanta International Airport |
| Washington Dulles International Airport |
| Suvarnabhumi Airport |
| Soekarno-Hatta International Airport |
| Sydney Airport |
| Logan International Airport |
| Detroit Metropolitan Wayne County Airport |
| Orlando International Airport |
| Kuala Lumpur International Airport |
| George Bush Intercontinental Airport |
| Melbourne Airport |
| Guangzhou Baiyun International Airport |
| Hong Kong International Airport |
| Beijing Capital International Airport |
| Miami International Airport |
| San Francisco International Airport |
| Seattle-Tacoma International Airport |
| Philadelphia International Airport |
| Amsterdam Airport Schiphol |
Input Table 2:
| Col_1 |
|:--------|
| PVG |
| LAX |
| CDG |
| CLT |
| JFK |
| NRT |
| DFW |
| MUC |
| SYD |
| BOM |
| BKK |
| KUL |
| MIA |
| HKG |
| AMS |
| BOS |
| BCN |
| IAD |
| SEA |
| DXB |
| MAD |
| IAH |
| CAN |
| FCO |
| MEL |
| SIN |
| YYZ |
| LGW |
| ORD |
| MEX |
| DTW |
| DUB |
| PHL |
| LHR |
| ORY |
| SFO |
| PHX |
| EWR |
| CGK |
| DEL |
| MSP |
| IST |
| DEN |
| FRA |
| ICN |
| PEK |
| MCO |
| ATL |
| HND |
| LAS |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case25", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case25", "case": "Case25", "label": [["Hartsfield-Jackson Atlanta International Airport", "ATL"], ["O'Hare International Airport", "ORD"], ["London Heathrow Airport", "LHR"], ["Tokyo International Airport", "HND"], ["Paris Charles de Gaulle Airport", "CDG"], ["Los Angeles International Airport", "LAX"], ["Dallas-Fort Worth International Airport", "DFW"], ["Beijing Capital International Airport", "PEK"], ["Frankfurt Airport", "FRA"], ["Denver International Airport", "DEN"], ["Madrid-Barajas Airport", "MAD"], ["Hong Kong International Airport", "HKG"], ["John F. Kennedy International Airport", "JFK"], ["Amsterdam Airport Schiphol", "AMS"], ["McCarran International Airport", "LAS"], ["George Bush Intercontinental Airport", "IAH"], ["Phoenix Sky Harbor International Airport", "PHX"], ["Suvarnabhumi Airport", "BKK"], ["Singapore Changi Airport", "SIN"], ["Dubai International Airport", "DXB"], ["San Francisco International Airport", "SFO"], ["Orlando International Airport", "MCO"], ["Newark Liberty International Airport", "EWR"], ["Detroit Metropolitan Wayne County Airport", "DTW"], ["Leonardo da Vinci Airport", "FCO"], ["Charlotte Douglas International Airport", "CLT"], ["Munich Airport", "MUC"], ["London Gatwick Airport", "LGW"], ["Miami International Airport", "MIA"], ["Minneapolis-Saint Paul International Airport", "MSP"], ["Narita International Airport", "NRT"], ["Guangzhou Baiyun International Airport", "CAN"], ["Sydney Airport", "SYD"], ["Toronto Pearson International Airport", "YYZ"], ["Seattle-Tacoma International Airport", "SEA"], ["Soekarno-Hatta International Airport", "CGK"], ["Philadelphia International Airport", "PHL"], ["Barcelona Airport", "BCN"], ["Seoul Incheon International Airport", "ICN"], ["Shanghai Pudong International Airport", "PVG"], ["Kuala Lumpur International Airport", "KUL"], ["Atat\u00c3\u00bcrk International Airport", "IST"], ["Mexico City International Airport", "MEX"], ["Paris-Orly Airport", "ORY"], ["Logan International Airport", "BOS"], ["Melbourne Airport", "MEL"], ["Chhatrapati Shivaji International Airport", "BOM"], ["Washington Dulles International Airport", "IAD"], ["Dublin Airport", "DUB"], ["Indira Gandhi International Airport", "DEL"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case25_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case25_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:---------------------|
| San Francisco 49ers |
| Houston Texans |
| Minnesota Vikings |
| Oakland Raiders |
| Philadelphia Eagles |
| Arizona Cardinals |
| Washington Redskins |
| New York Giants |
| San Diego Chargers |
| Atlanta Falcons |
| Kansas City Chiefs |
| Buffalo Bills |
| New Orleans Saints |
| Jacksonville Jaguars |
| Tampa Bay Buccaneers |
| Baltimore Ravens |
| Dallas Cowboys |
| Green Bay Packers |
| Cincinnati Bengals |
| New York Jets |
| Seattle Seahawks |
| Pittsburgh Steelers |
| Denver Broncos |
| Carolina Panthers |
| New England Patriots |
| St. Louis Rams |
| Miami Dolphins |
| Indianapolis Colts |
| Cleveland Browns |
| Chicago Bears |
| Tennessee Titans |
| Detroit Lions |
Input Table 2:
| Col_1 |
|:---------------|
| North Carolina |
| New York |
| Indiana |
| Tennessee |
| Minnesota |
| California |
| Florida |
| New Jersey |
| Washington |
| Pennsylvania |
| Michigan |
| Texas |
| Colorado |
| Massachusetts |
| Illinois |
| Missouri |
| Louisiana |
| Ohio |
| Georgia |
| Maryland |
| Wisconsin |
| Arizona |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case32", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case32", "case": "Case32", "label": [["Washington Redskins", "Maryland"], ["New York Jets", "New Jersey"], ["New York Giants", "New Jersey"], ["Dallas Cowboys", "Texas"], ["Kansas City Chiefs", "Missouri"], ["Denver Broncos", "Colorado"], ["Miami Dolphins", "Florida"], ["Carolina Panthers", "North Carolina"], ["New Orleans Saints", "Louisiana"], ["Cleveland Browns", "Ohio"], ["Green Bay Packers", "Wisconsin"], ["Buffalo Bills", "New York"], ["Atlanta Falcons", "Georgia"], ["Houston Texans", "Texas"], ["Baltimore Ravens", "Maryland"], ["San Diego Chargers", "California"], ["San Francisco 49ers", "California"], ["Philadelphia Eagles", "Pennsylvania"], ["Tennessee Titans", "Tennessee"], ["New England Patriots", "Massachusetts"], ["Jacksonville Jaguars", "Florida"], ["Seattle Seahawks", "Washington"], ["St. Louis Rams", "Missouri"], ["Tampa Bay Buccaneers", "Florida"], ["Cincinnati Bengals", "Ohio"], ["Pittsburgh Steelers", "Pennsylvania"], ["Detroit Lions", "Michigan"], ["Minnesota Vikings", "Minnesota"], ["Arizona Cardinals", "Arizona"], ["Oakland Raiders", "California"], ["Indianapolis Colts", "Indiana"], ["Chicago Bears", "Illinois"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case32_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case32_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:--------|
| TRIP |
| LNG |
| YHOO |
| SLB |
| WYNN |
| SLH |
| PFE |
| NUAN |
| DFS |
| MDCA |
| SLM |
| HELE |
| ODP |
| CMG |
| NBR |
| ZNGA |
| HON |
| SIRI |
| DOW |
| RE |
| TWX |
| LOW |
| RH |
| XOM |
| FCX |
| RL |
| VIAB |
| BA |
| CVS |
| STRZA |
| CELG |
| CVX |
| F |
| MRO |
| THC |
| FLT |
| DHR |
| P |
| SD |
| S |
| ABT |
| T |
| QCOM |
| WFC |
| V |
| KO |
| COP |
| CMLS |
| DIS |
| BMY |
| GGP |
| HAL |
| MINI |
| MCK |
| LNKD |
| HAS |
| REGN |
| ETN |
| DE |
| SPB |
| CHK |
| PAY |
| RLGY |
| GPS |
| TRW |
| UTX |
| AMG |
| MO |
| TJX |
| AMP |
| EL |
| ORCL |
| EMR |
| DISCA |
| AET |
| ROP |
| HCN |
| PSX |
| BXP |
| NLSN |
| ENH |
| CRM |
| AVGO |
| CSCO |
| UTHR |
| GBL |
| CSC |
| CBS |
| TDG |
| GD |
| GE |
| GS |
| GT |
| TMUS |
| JAH |
| LMT |
| CMCSA |
| PH |
| AXP |
| HAIN |
Input Table 2:
| Col_1 |
|:-----------------------------|
| FREEPORT-MCMORAN COP&GOLD |
| HEALTH CARE REIT INC |
| EATON CORP PLC |
| TIME WARNER INC |
| BOEING CO |
| GAMCO INVESTORS INC |
| VERIFONE SYSTEMS INC |
| PFIZER INC |
| HAIN CELESTIAL GROUP INC |
| DISCOVERY COMMUNICATIONS INC |
| TRW AUTOMOTIVE HOLDINGS CORP |
| SIRIUS XM HOLDINGS INC |
| HONEYWELL INTERNATIONAL INC |
| SCHLUMBERGER LTD |
| AMERICAN EXPRESS CO |
| LINKEDIN CORP |
| SOLERA HOLDINGS INC |
| GENERAL ELECTRIC CO |
| QUALCOMM INC |
| CUMULUS MEDIA INC |
| TJX COMPANIES INC |
| DISNEY (WALT) CO |
| EXXON MOBIL CORP |
| WYNN RESORTS LTD |
| AT&T INC |
| MARATHON OIL CORP |
| AMERIPRISE FINANCIAL INC |
| CHEVRON CORP |
| TRIPADVISOR INC |
| FORD MOTOR CO |
| YAHOO INC |
| T-MOBILE US INC |
| ABBOTT LABORATORIES |
| HALLIBURTON CO |
| UNITED TECHNOLOGIES CORP |
| SLM CORP -SPN |
| PANDORA MEDIA INC |
| SPECTRUM BRANDS HOLDINGS INC |
| TENET HEALTHCARE CORP |
| VIACOM INC |
| GAP INC |
| SPRINT CORP |
| NABORS INDUSTRIES LTD |
| NUANCE COMMUNICATIONS INC |
| ORACLE CORP |
| ZYNGA INC |
| CISCO SYSTEMS INC |
| CVS CAREMARK CORP |
| WELLS FARGO & CO |
| DISCOVER FINANCIAL SVCS INC |
| JARDEN CORP |
| NIELSEN HOLDINGS NV |
| MDC PARTNERS INC |
| CELGENE CORP |
| UNITED THERAPEUTICS CORP |
| COCA-COLA CO |
| DOW CHEMICAL |
| MCKESSON CORP |
| MOBILE MINI INC |
| COMPUTER SCIENCES CORP |
| ALTRIA GROUP INC |
| RALPH LAUREN CORP |
| REGENERON PHARMACEUTICALS |
| BRISTOL-MYERS SQUIBB CO |
| COMCAST CORP |
| CBS CORP |
| LOWE'S COMPANIES INC |
| CONOCOPHILLIPS |
| AFFILIATED MANAGERS GRP INC |
| AVAGO TECHNOLOGIES LTD |
| ROPER INDUSTRIES INC |
| VISA INC |
| PHILLIPS 66 |
| LAUDER (ESTEE) COS INC |
| DANAHER CORP |
| CHIPOTLE MEXICAN GRILL INC |
| GENERAL DYNAMICS CORP |
| TRANSDIGM GROUP INC |
| HASBRO INC |
| HELEN OF TROY LTD |
| FLEETCOR TECHNOLOGIES INC |
| AETNA INC |
| CHENIERE ENERGY INC |
| SANDRIDGE ENERGY INC |
| SALESFORCE.COM INC |
| PARKER-HANNIFIN CORP |
| EMERSON ELECTRIC CO |
| REALOGY HOLDINGS CORP |
| CHESAPEAKE ENERGY CORP |
| ENDURANCE SPECIALTY HOLDINGS |
| GOODYEAR TIRE & RUBBER CO |
| OFFICE DEPOT INC |
| RESTORATION HARDWARE HLDNGS |
| STARZ |
| LOCKHEED MARTIN CORP |
| GOLDMAN SACHS GROUP INC |
| EVEREST RE GROUP LTD |
| DEERE & CO |
| BOSTON PROPERTIES INC |
| GENERAL GROWTH PPTYS INC |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case20", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case20", "case": "Case20", "label": [["LNG", "CHENIERE ENERGY INC"], ["GBL", "GAMCO INVESTORS INC"], ["ORCL", "ORACLE CORP"], ["SD", "SANDRIDGE ENERGY INC"], ["NBR", "NABORS INDUSTRIES LTD"], ["CBS", "CBS CORP"], ["TDG", "TRANSDIGM GROUP INC"], ["ZNGA", "ZYNGA INC"], ["FCX", "FREEPORT-MCMORAN COP&GOLD"], ["MCK", "MCKESSON CORP"], ["S", "SPRINT CORP"], ["LNKD", "LINKEDIN CORP"], ["ENH", "ENDURANCE SPECIALTY HOLDINGS"], ["HELE", "HELEN OF TROY LTD"], ["TRIP", "TRIPADVISOR INC"], ["UTHR", "UNITED THERAPEUTICS CORP"], ["VIAB", "VIACOM INC"], ["RH", "RESTORATION HARDWARE HLDNGS"], ["REGN", "REGENERON PHARMACEUTICALS"], ["DIS", "DISNEY (WALT) CO"], ["DISCA", "DISCOVERY COMMUNICATIONS INC"], ["FLT", "FLEETCOR TECHNOLOGIES INC"], ["TWX", "TIME WARNER INC"], ["AVGO", "AVAGO TECHNOLOGIES LTD"], ["EL", "LAUDER (ESTEE) COS INC"], ["CMCSA", "COMCAST CORP"], ["CRM", "SALESFORCE.COM INC"], ["CVS", "CVS CAREMARK CORP"], ["AET", "AETNA INC"], ["STRZA", "STARZ"], ["SLH", "SOLERA HOLDINGS INC"], ["TMUS", "T-MOBILE US INC"], ["NUAN", "NUANCE COMMUNICATIONS INC"], ["P", "PANDORA MEDIA INC"], ["XOM", "EXXON MOBIL CORP"], ["PH", "PARKER-HANNIFIN CORP"], ["HAS", "HASBRO INC"], ["HAIN", "HAIN CELESTIAL GROUP INC"], ["RL", "RALPH LAUREN CORP"], ["HON", "HONEYWELL INTERNATIONAL INC"], ["EMR", "EMERSON ELECTRIC CO"], ["LMT", "LOCKHEED MARTIN CORP"], ["CMG", "CHIPOTLE MEXICAN GRILL INC"], ["YHOO", "YAHOO INC"], ["TRW", "TRW AUTOMOTIVE HOLDINGS CORP"], ["V", "VISA INC"], ["MINI", "MOBILE MINI INC"], ["CVX", "CHEVRON CORP"], ["RLGY", "REALOGY HOLDINGS CORP"], ["BXP", "BOSTON PROPERTIES INC"], ["COP", "CONOCOPHILLIPS"], ["MRO", "MARATHON OIL CORP"], ["BA", "BOEING CO"], ["T", "AT&T INC"], ["F", "FORD MOTOR CO"], ["ETN", "EATON CORP PLC"], ["SIRI", "SIRIUS XM HOLDINGS INC"], ["SLB", "SCHLUMBERGER LTD"], ["THC", "TENET HEALTHCARE CORP"], ["TJX", "TJX COMPANIES INC"], ["CHK", "CHESAPEAKE ENERGY CORP"], ["GGP", "GENERAL GROWTH PPTYS INC"], ["AXP", "AMERICAN EXPRESS CO"], ["ROP", "ROPER INDUSTRIES INC"], ["CSC", "COMPUTER SCIENCES CORP"], ["RE", "EVEREST RE GROUP LTD"], ["DFS", "DISCOVER FINANCIAL SVCS INC"], ["CSCO", "CISCO SYSTEMS INC"], ["CELG", "CELGENE CORP"], ["ABT", "ABBOTT LABORATORIES"], ["HAL", "HALLIBURTON CO"], ["BMY", "BRISTOL-MYERS SQUIBB CO"], ["MDCA", "MDC PARTNERS INC"], ["DOW", "DOW CHEMICAL"], ["QCOM", "QUALCOMM INC"], ["PAY", "VERIFONE SYSTEMS INC"], ["KO", "COCA-COLA CO"], ["UTX", "UNITED TECHNOLOGIES CORP"], ["SLM", "SLM CORP -SPN"], ["MO", "ALTRIA GROUP INC"], ["AMG", "AFFILIATED MANAGERS GRP INC"], ["CMLS", "CUMULUS MEDIA INC"], ["GS", "GOLDMAN SACHS GROUP INC"], ["HCN", "HEALTH CARE REIT INC"], ["PSX", "PHILLIPS 66"], ["GE", "GENERAL ELECTRIC CO"], ["AMP", "AMERIPRISE FINANCIAL INC"], ["DHR", "DANAHER CORP"], ["ODP", "OFFICE DEPOT INC"], ["WYNN", "WYNN RESORTS LTD"], ["NLSN", "NIELSEN HOLDINGS NV"], ["WFC", "WELLS FARGO & CO"], ["GT", "GOODYEAR TIRE & RUBBER CO"], ["DE", "DEERE & CO"], ["JAH", "JARDEN CORP"], ["GD", "GENERAL DYNAMICS CORP"], ["PFE", "PFIZER INC"], ["SPB", "SPECTRUM BRANDS HOLDINGS INC"], ["GPS", "GAP INC"], ["LOW", "LOWE'S COMPANIES INC"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case20_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case20_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:---------------------------------------------|
| Backstreet's Back / Backstreet Boys |
| No Jacket Required |
| CrazySexyCool |
| The Woman in Me |
| True Blue |
| Happy Nation/The Sign |
| Can't Slow Down |
| Confessions |
| The Marshall Mathers LP |
| Purple Rain |
| Millennium |
| Nevermind |
| The Joshua Tree |
| Boston |
| Whitney Houston |
| Hysteria |
| Grease |
| (What's the Story) Morning Glory? |
| Spice |
| Cross Road |
| HIStory: Past, Present and Future, Book I |
| Tapestry |
| Hybrid Theory |
| Slippery When Wet |
| Faith |
| Private Dancer |
| ABBA Gold: Greatest Hits |
| Bridge over Troubled Water |
| Like a Virgin |
| Breakfast in America |
| Whitney |
| Oops!... I Did It Again |
| Off the Wall |
| Legend: The Best of Bob Marley & The Wailers |
| Daydream |
| Greatest Hits |
| Guilty |
| ...Baby One More Time |
| Come Away with Me |
| 21 |
| Unplugged |
Input Table 2:
| Col_1 |
|:-------------------------|
| Simon & Garfunkel |
| Supertramp |
| Nirvana |
| Carole King |
| TLC |
| Tina Turner |
| Bon Jovi |
| Lionel Richie |
| Backstreet Boys |
| Shania Twain |
| Various artists |
| Linkin Park |
| Phil Collins |
| Boston |
| Bob Marley & The Wailers |
| Whitney Houston |
| Queen |
| Barbra Streisand |
| Def Leppard |
| Oasis |
| Eric Clapton |
| Michael Jackson |
| Spice Girls |
| Norah Jones |
| George Michael |
| Prince & The Revolution |
| Usher |
| Mariah Carey |
| Britney Spears |
| Adele |
| Madonna |
| Eminem |
| Ace of Base |
| U2 |
| ABBA |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case28", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case28", "case": "Case28", "label": [["ABBA Gold: Greatest Hits", "ABBA"], ["Backstreet's Back / Backstreet Boys", "Backstreet Boys"], ["Millennium", "Backstreet Boys"], ["Slippery When Wet", "Bon Jovi"], ["Spice", "Spice Girls"], ["Grease", "Various artists"], ["21", "Adele"], ["...Baby One More Time", "Britney Spears"], ["Nevermind", "Nirvana"], ["Come Away with Me", "Norah Jones"], ["Legend: The Best of Bob Marley & The Wailers", "Bob Marley & The Wailers"], ["Tapestry", "Carole King"], ["True Blue", "Madonna"], ["Daydream", "Mariah Carey"], ["Greatest Hits", "Queen"], ["Bridge over Troubled Water", "Simon & Garfunkel"], ["The Joshua Tree", "U2"], ["Whitney Houston", "Whitney Houston"], ["Whitney", "Whitney Houston"], ["Hybrid Theory", "Linkin Park"], ["Happy Nation/The Sign", "Ace of Base"], ["CrazySexyCool", "TLC"], ["(What's the Story) Morning Glory?", "Oasis"], ["Cross Road", "Bon Jovi"], ["Like a Virgin", "Madonna"], ["Guilty", "Barbra Streisand"], ["Boston", "Boston"], ["Oops!... I Did It Again", "Britney Spears"], ["Hysteria", "Def Leppard"], ["Faith", "George Michael"], ["The Marshall Mathers LP", "Eminem"], ["Unplugged", "Eric Clapton"], ["Can't Slow Down", "Lionel Richie"], ["HIStory: Past, Present and Future, Book I", "Michael Jackson"], ["Off the Wall", "Michael Jackson"], ["No Jacket Required", "Phil Collins"], ["Purple Rain", "Prince & The Revolution"], ["The Woman in Me", "Shania Twain"], ["Breakfast in America", "Supertramp"], ["Private Dancer", "Tina Turner"], ["Confessions", "Usher"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case28_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case28_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:---------------------------------------------------|
| Louis Armstrong New Orleans International Airport |
| Palm Beach International Airport |
| Austin-Bergstrom International Airport |
| Ted Stevens Anchorage International Airport |
| Theodore Francis Green State Airport |
| Cleveland Hopkins International Airport |
| Bradley International Airport |
| Memphis International Airport |
| John Wayne Airport |
| Ontario International Airport |
| Jacksonville International Airport |
| Cincinnati/Northern Kentucky International Airport |
| Albuquerque International Sunport |
| Bob Hope Airport |
| Nashville International Airport |
| Indianapolis International Airport |
| Raleigh-Durham International Airport |
| Buffalo Niagara International Airport |
| Lambert–St. Louis International Airport |
| Pittsburgh International Airport |
| San Jose International Airport |
| William P. Hobby Airport |
| San Antonio International Airport |
| Luis Muñoz Marín International Airport |
| Metropolitan Oakland International Airport |
| Sacramento International Airport |
| Dallas Love Field |
| Port Columbus International Airport |
| Kahului Airport |
| Eppley Airfield |
| Southwest Florida International Airport |
| General Mitchell International Airport |
| Kansas City International Airport |
Input Table 2:
| Col_1 |
|:--------|
| HI |
| MO |
| PR |
| TX |
| IN |
| FL |
| AK |
| NY |
| PA |
| CT |
| WI |
| LA |
| NC |
| NE |
| RI |
| OH |
| TN |
| CA |
| NM |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case44", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case44", "case": "Case44", "label": [["Lambert\u2013St. Louis International Airport", "MO"], ["William P. Hobby Airport", "TX"], ["Nashville International Airport", "TN"], ["Austin-Bergstrom International Airport", "TX"], ["Kansas City International Airport", "MO"], ["Metropolitan Oakland International Airport", "CA"], ["Louis Armstrong New Orleans International Airport", "LA"], ["John Wayne Airport", "CA"], ["Raleigh-Durham International Airport", "NC"], ["Cleveland Hopkins International Airport", "OH"], ["San Jose International Airport", "CA"], ["Sacramento International Airport", "CA"], ["Luis Mu\u00f1oz Mar\u00edn International Airport", "PR"], ["Dallas Love Field", "TX"], ["San Antonio International Airport", "TX"], ["Pittsburgh International Airport", "PA"], ["Southwest Florida International Airport", "FL"], ["Indianapolis International Airport", "IN"], ["General Mitchell International Airport", "WI"], ["Port Columbus International Airport", "OH"], ["Kahului Airport", "HI"], ["Palm Beach International Airport", "FL"], ["Cincinnati/Northern Kentucky International Airport", "OH"], ["Bradley International Airport", "CT"], ["Buffalo Niagara International Airport", "NY"], ["Jacksonville International Airport", "FL"], ["Albuquerque International Sunport", "NM"], ["Ted Stevens Anchorage International Airport", "AK"], ["Memphis International Airport", "TN"], ["Eppley Airfield", "NE"], ["Ontario International Airport", "CA"], ["Bob Hope Airport", "CA"], ["Theodore Francis Green State Airport", "RI"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case44_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case44_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:-------------------------------------------------|
| Denver International Airport |
| Leonardo da Vinci Airport |
| Barcelona Airport |
| O'Hare International Airport |
| Munich Airport |
| Phoenix Sky Harbor International Airport |
| John F. Kennedy International Airport |
| Minneapolis-Saint Paul International Airport |
| Atatürk International Airport |
| Dubai International Airport |
| Los Angeles International Airport |
| London Gatwick Airport |
| Toronto Pearson International Airport |
| Paris Charles de Gaulle Airport |
| Shanghai Pudong International Airport |
| Singapore Changi Airport |
| Seoul Incheon International Airport |
| London Heathrow Airport |
| Paris-Orly Airport |
| Frankfurt Airport |
| Newark Liberty International Airport |
| Charlotte Douglas International Airport |
| Dublin Airport |
| McCarran International Airport |
| Dallas-Fort Worth International Airport |
| Mexico City International Airport |
| Narita International Airport |
| Indira Gandhi International Airport |
| Tokyo International Airport |
| Madrid-Barajas Airport |
| Chhatrapati Shivaji International Airport |
| Hartsfield-Jackson Atlanta International Airport |
| Washington Dulles International Airport |
| Suvarnabhumi Airport |
| Soekarno-Hatta International Airport |
| Sydney Airport |
| Logan International Airport |
| Detroit Metropolitan Wayne County Airport |
| Orlando International Airport |
| Kuala Lumpur International Airport |
| George Bush Intercontinental Airport |
| Melbourne Airport |
| Guangzhou Baiyun International Airport |
| Hong Kong International Airport |
| Beijing Capital International Airport |
| Miami International Airport |
| San Francisco International Airport |
| Seattle-Tacoma International Airport |
| Philadelphia International Airport |
| Amsterdam Airport Schiphol |
Input Table 2:
| Col_1 |
|:--------|
| EGLL |
| VABB |
| KPHI |
| CYYZ |
| KSFO |
| WSSS |
| KPHX |
| KEWR |
| EIDW |
| VHHH |
| YMML |
| KMSP |
| RJTT |
| WMKK |
| LEBL |
| EGKK |
| KIAD |
| KIAH |
| VTBS |
| KSEA |
| YSSY |
| EHAM |
| KORD |
| KDTW |
| EDDF |
| KCLT |
| KJFK |
| ZSPD |
| EDDM |
| KDFW |
| LFPG |
| LIRF |
| VIDP |
| KBOS |
| KMIA |
| WIII |
| LFPO |
| ZBAA |
| KDEN |
| ZGGG |
| LTBA |
| RKSI |
| KATL |
| KMCO |
| MMMX |
| KLAS |
| LEMD |
| OMDB |
| KLAX |
| RJAA |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case26", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case26", "case": "Case26", "label": [["Hartsfield-Jackson Atlanta International Airport", "KATL"], ["O'Hare International Airport", "KORD"], ["London Heathrow Airport", "EGLL"], ["Tokyo International Airport", "RJTT"], ["Paris Charles de Gaulle Airport", "LFPG"], ["Los Angeles International Airport", "KLAX"], ["Dallas-Fort Worth International Airport", "KDFW"], ["Beijing Capital International Airport", "ZBAA"], ["Frankfurt Airport", "EDDF"], ["Denver International Airport", "KDEN"], ["Madrid-Barajas Airport", "LEMD"], ["Hong Kong International Airport", "VHHH"], ["John F. Kennedy International Airport", "KJFK"], ["Amsterdam Airport Schiphol", "EHAM"], ["McCarran International Airport", "KLAS"], ["George Bush Intercontinental Airport", "KIAH"], ["Phoenix Sky Harbor International Airport", "KPHX"], ["Suvarnabhumi Airport", "VTBS"], ["Singapore Changi Airport", "WSSS"], ["Dubai International Airport", "OMDB"], ["San Francisco International Airport", "KSFO"], ["Orlando International Airport", "KMCO"], ["Newark Liberty International Airport", "KEWR"], ["Detroit Metropolitan Wayne County Airport", "KDTW"], ["Leonardo da Vinci Airport", "LIRF"], ["Charlotte Douglas International Airport", "KCLT"], ["Munich Airport", "EDDM"], ["London Gatwick Airport", "EGKK"], ["Miami International Airport", "KMIA"], ["Minneapolis-Saint Paul International Airport", "KMSP"], ["Narita International Airport", "RJAA"], ["Guangzhou Baiyun International Airport", "ZGGG"], ["Sydney Airport", "YSSY"], ["Toronto Pearson International Airport", "CYYZ"], ["Seattle-Tacoma International Airport", "KSEA"], ["Soekarno-Hatta International Airport", "WIII"], ["Philadelphia International Airport", "KPHI"], ["Barcelona Airport", "LEBL"], ["Seoul Incheon International Airport", "RKSI"], ["Shanghai Pudong International Airport", "ZSPD"], ["Kuala Lumpur International Airport", "WMKK"], ["Atat\u00c3\u00bcrk International Airport", "LTBA"], ["Mexico City International Airport", "MMMX"], ["Paris-Orly Airport", "LFPO"], ["Logan International Airport", "KBOS"], ["Melbourne Airport", "YMML"], ["Chhatrapati Shivaji International Airport", "VABB"], ["Washington Dulles International Airport", "KIAD"], ["Dublin Airport", "EIDW"], ["Indira Gandhi International Airport", "VIDP"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case26_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case26_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:---------------------|
| Martin Van Buren |
| John Adams |
| Hubert M. Humphrey |
| Elbridge Gerry |
| Charles G. Dawes |
| Dan Quayle |
| William A. Wheeler |
| Gerald R. Ford |
| Charles W. Fairbanks |
| Thomas Jefferson |
| Andrew Johnson |
| Schuyler Colfax |
| Nelson Rockefeller |
| Thomas R. Marshall |
| John Tyler |
| Henry Wilson |
| Richard Cheney |
| Daniel D. Tompkins |
| Henry A. Wallace |
| Aaron Burr |
| Hannibal Hamlin |
| Charles Curtis |
| Chester A. Arthur |
| Richard M. Nixon |
| Walter F. Mondale |
| Richard M. Johnson |
| Millard Fillmore |
| Calvin Coolidge |
| Levi P. Morton |
| William R. King |
| Adlai E. Stevenson |
| John C. Breckinridge |
| Harry S. Truman |
| Albert Gore |
| Garret A. Hobart |
| Theodore Roosevelt |
| James S. Sherman |
| John N. Garner |
| Lyndon B. Johnson |
| Joseph R. Biden |
| George M. Dallas |
| Barkley, Alben W |
| Spiro T. Agnew |
| George Bush |
| Thomas A. Hendricks |
Input Table 2:
| Col_1 |
|:--------------------------|
| Martin Van Buren |
| William Henry Harrison |
| George Washington |
| John Adams |
| Zachary Taylor |
| Dwight D. Eisenhower |
| Herbert Hoover |
| James A. Garfield |
| William H. Taft |
| Bill Clinton |
| John F. Kennedy |
| Gerald R. Ford |
| George W. Bush |
| Thomas Jefferson |
| Franklin D. Roosevelt |
| Jimmy Carter |
| James K. Polk |
| James Madison |
| Rutherford Birchard Hayes |
| Ulysses S. Grant |
| Andrew Jackson |
| Ronald Reagan |
| Woodrow Wilson |
| Grover Cleveland |
| James Buchanan |
| Warren G. Harding |
| Benjamin Harrison |
| Richard M. Nixon |
| Franklin Pierce |
| Abraham Lincoln |
| Calvin Coolidge |
| Barack Obama |
| James Monroe |
| Harry S. Truman |
| William McKinley |
| Theodore Roosevelt |
| Lyndon B. Johnson |
| George Bush |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case43", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case43", "case": "Case43", "label": [["John Adams", "George Washington"], ["Thomas Jefferson", "John Adams"], ["Aaron Burr", "Thomas Jefferson"], ["Elbridge Gerry", "James Madison"], ["Daniel D. Tompkins", "James Monroe"], ["Martin Van Buren", "Andrew Jackson"], ["Richard M. Johnson", "Martin Van Buren"], ["John Tyler", "William Henry Harrison"], ["George M. Dallas", "James K. Polk"], ["Millard Fillmore", "Zachary Taylor"], ["William R. King", "Franklin Pierce"], ["John C. Breckinridge", "James Buchanan"], ["Hannibal Hamlin", "Abraham Lincoln"], ["Andrew Johnson", "Abraham Lincoln"], ["Schuyler Colfax", "Ulysses S. Grant"], ["Henry Wilson", "Ulysses S. Grant"], ["William A. Wheeler", "Rutherford Birchard Hayes"], ["Chester A. Arthur", "James A. Garfield"], ["Thomas A. Hendricks", "Grover Cleveland"], ["Levi P. Morton", "Benjamin Harrison"], ["Adlai E. Stevenson", "Grover Cleveland"], ["Garret A. Hobart", "William McKinley"], ["Theodore Roosevelt", "William McKinley"], ["Charles W. Fairbanks", "Theodore Roosevelt"], ["James S. Sherman", "William H. Taft"], ["Thomas R. Marshall", "Woodrow Wilson"], ["Calvin Coolidge", "Warren G. Harding"], ["Charles G. Dawes", "Calvin Coolidge"], ["Charles Curtis", "Herbert Hoover"], ["John N. Garner", "Franklin D. Roosevelt"], ["Henry A. Wallace", "Franklin D. Roosevelt"], ["Harry S. Truman", "Franklin D. Roosevelt"], ["Barkley, Alben W", "Harry S. Truman"], ["Richard M. Nixon", "Dwight D. Eisenhower"], ["Lyndon B. Johnson", "John F. Kennedy"], ["Hubert M. Humphrey", "Lyndon B. Johnson"], ["Spiro T. Agnew", "Richard M. Nixon"], ["Gerald R. Ford", "Richard M. Nixon"], ["Nelson Rockefeller", "Gerald R. Ford"], ["Walter F. Mondale", "Jimmy Carter"], ["George Bush", "Ronald Reagan"], ["Dan Quayle", "George Bush"], ["Albert Gore", "Bill Clinton"], ["Richard Cheney", "George W. Bush"], ["Joseph R. Biden", "Barack Obama"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case43_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case43_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:---------------------------------|
| Papua New Guinea |
| Cambodia |
| Kazakhstan |
| Paraguay |
| Syria |
| Solomon Islands |
| Mali |
| Marshall Islands |
| Panama |
| Laos |
| Argentina |
| Seychelles |
| Belize |
| Zambia |
| Bahrain |
| Guinea-Bissau |
| Namibia |
| Comoros |
| Finland |
| Georgia |
| Saint Kitts and Nevis |
| Yemen |
| Eritrea |
| Madagascar |
| Libya |
| Sweden |
| Malawi |
| Andorra |
| Liechtenstein |
| Poland |
| Bulgaria |
| Jordan |
| Tunisia |
| Tuvalu |
| United Arab Emirates |
| Kenya |
| Brunei |
| Djibouti |
| Lebanon |
| Azerbaijan |
| Cuba |
| Czech Republic |
| Mauritania |
| Saint Lucia |
| Israel |
| San Marino |
| Australia |
| Tajikistan |
| Cameroon |
| Cyprus |
| Malaysia |
| Iceland |
| Oman |
| Armenia |
| Gabon |
| Luxembourg |
| Brazil |
| Algeria |
| Slovenia |
| Republic of the Congo |
| Antigua and Barbuda |
| Colombia |
| Ecuador |
| Moldova |
| Vanuatu |
| Honduras |
| Italy |
| Nauru |
| Haiti |
| Afghanistan |
| Burundi |
| Singapore |
| Federated States of Micronesia |
| Vatican City |
| Russia |
| Netherlands |
| China |
| Kyrgyzstan |
| Bhutan |
| Romania |
| Togo |
| Philippines |
| Cote d'Ivoire |
| Uzbekistan |
| Zimbabwe |
| Dominica |
| Indonesia |
| Benin |
| Angola |
| East Timor |
| Sudan |
| Portugal |
| Grenada |
| North Korea |
| Greece |
| Latvia |
| Mongolia |
| Iran |
| Morocco |
| Guatemala |
| Guyana |
| Iraq |
| Chile |
| Nepal |
| The Gambia |
| Tanzania |
| Ukraine |
| Ghana |
| India |
| Canada |
| Maldives |
| Turkey |
| Belgium |
| The Bahamas |
| Taiwan |
| South Africa |
| Trinidad and Tobago |
| Central African Republic |
| Democratic Republic of the Congo |
| Jamaica |
| Peru |
| Turkmenistan |
| Germany |
| Fiji |
| Guinea |
| United States |
| Chad |
| Somalia |
| Sao Tome and Principe |
| Thailand |
| Equatorial Guinea |
| Kiribati |
| Costa Rica |
| Vietnam |
| Kuwait |
| Nigeria |
| Croatia |
| Sri Lanka |
| Uruguay |
| United Kingdom |
| Switzerland |
| Samoa |
| Spain |
| Liberia |
| Venezuela |
| Burkina Faso |
| Swaziland |
| Palau |
| Estonia |
| Austria |
| South Korea |
| Mozambique |
| El Salvador |
| Monaco |
| Lesotho |
| Tonga |
| Hungary |
| Japan |
| Belarus |
| Mauritius |
| Albania |
| New Zealand |
| Senegal |
| Macedonia |
| Ethiopia |
| Egypt |
| Sierra Leone |
| Bolivia |
| Malta |
| Saudi Arabia |
| Cape Verde |
| Pakistan |
| Ireland |
| Qatar |
| Serbia and Montenegro |
| Slovakia |
| France |
| Lithuania |
| Bosnia and Herzegovina |
| Niger |
| Rwanda |
| Burma |
| Bangladesh |
| Barbados |
| Nicaragua |
| Norway |
| Botswana |
| Saint Vincent and the Grenadines |
| Denmark |
| Dominican Republic |
| Mexico |
| Uganda |
| Suriname |
Input Table 2:
| Col_1 |
|:--------------------|
| Belgrade |
| Maputo |
| Male |
| Tashkent |
| Beirut |
| San Jose |
| Port-Vila |
| Baku |
| Banjul |
| Ottawa |
| Chisinau |
| Warsaw |
| Lisbon |
| Valletta |
| Budapest |
| London |
| Muscat |
| Nassau |
| Yaounde |
| Phnom Penh |
| Amman |
| Helsinki |
| Havana |
| Port Moresby |
| Lome |
| Minsk |
| La Paz |
| Managua |
| Mogadishu |
| Tbilisi |
| Djibouti |
| Kampala |
| Jakarta |
| San Marino |
| Nuku'alofa |
| Mexico City |
| Mbabana |
| Antananarivo |
| Suva |
| Kiev |
| Kingstown |
| Ashgabat |
| Majuro |
| Rome |
| Nairobi |
| Bishtek |
| Tokyo |
| Luxembourg |
| Sarajevo |
| Oslo |
| Jerusalem |
| Bratislava |
| Prague |
| Baghdad |
| Yerevan |
| Dili |
| Singapore |
| Doha |
| Rangoon |
| Bangui |
| Sofia |
| Taipei |
| Tallinn |
| Kingston |
| Manila |
| Moroni |
| Kuala Lumpur |
| Asuncion |
| Vatican City |
| Reykjavik |
| Manama |
| Yamoussoukro |
| Malabo |
| Luanda |
| Pyongyang |
| Sao Tome |
| Santo Domingo |
| Nouakchott |
| Panama City |
| Kinshasa |
| Skopje |
| Cairo |
| Ankara |
| Tegucigalpa |
| Yaren District |
| Bamako |
| Tunis |
| Castries |
| Bucharest |
| Kabul |
| Copenhagen |
| Brazzaville |
| Kathmandu |
| Lusaka |
| Nicosia |
| Georgetown |
| Wellington |
| Andorra la Vella |
| Victoria |
| Addis Ababa |
| Freetown |
| Bandar Seri Begawan |
| Athens |
| Hanoi |
| Abu Dhabi |
| Beijing |
| Bangkok |
| Santiago |
| Niamey |
| N'Djamena |
| Madrid |
| Maseru |
| Canberra |
| Thimphu |
| Khartoum |
| Algiers |
| Saint George's |
| Astana |
| Abuja |
| Praia |
| Quito |
| Moscow |
| Windhoek |
| Harare |
| Amsterdam |
| Seoul |
| Riga |
| Libreville |
| Buenos Aires |
| Washington D.C. |
| Palikir |
| Sanaa |
| Funafuti |
| Asmara |
| Islamabad |
| Tirane |
| Vientiane |
| Bridgetown |
| Brasilia |
| Ulaanbaatar |
| Koror |
| Honiara |
| Dublin |
| San Salvador |
| Riyadh |
| Belmopan |
| Accra |
| Zagreb |
| Monaco |
| Porto-Novo |
| Brussels |
| Tarawa |
| Ouagadougou |
| Berlin |
| Kigali |
| Paramaribo |
| Port Louis |
| Dar es Salaam |
| Bogota |
| Stockholm |
| Guatemala City |
| New Delhi |
| Caracas |
| Conakry |
| Vaduz |
| Vilnius |
| Apia |
| Dhaka |
| Bern |
| Lilongwe |
| Bujumbura |
| Damascus |
| Gaborone |
| Basseterre |
| Port-of-Spain |
| Lima |
| Tripoli |
| Rabat |
| Vienna |
| Pretoria |
| Colombo |
| Dushanbe |
| Roseau |
| Montevideo |
| Tehran |
| Bissau |
| Ljubljana |
| Dakar |
| Monrovia |
| Port-au-Prince |
| Kuwait City |
| Saint John's |
| Paris |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case10", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case10", "case": "Case10", "label": [["Afghanistan", "Kabul"], ["Albania", "Tirane"], ["Algeria", "Algiers"], ["Andorra", "Andorra la Vella"], ["Angola", "Luanda"], ["Antigua and Barbuda", "Saint John's"], ["Argentina", "Buenos Aires"], ["Armenia", "Yerevan"], ["Australia", "Canberra"], ["Austria", "Vienna"], ["Azerbaijan", "Baku"], ["Bahrain", "Manama"], ["Bangladesh", "Dhaka"], ["Barbados", "Bridgetown"], ["Belarus", "Minsk"], ["Belgium", "Brussels"], ["Belize", "Belmopan"], ["Benin", "Porto-Novo"], ["Bhutan", "Thimphu"], ["Bolivia", "La Paz"], ["Bosnia and Herzegovina", "Sarajevo"], ["Botswana", "Gaborone"], ["Brazil", "Brasilia"], ["Brunei", "Bandar Seri Begawan"], ["Bulgaria", "Sofia"], ["Burkina Faso", "Ouagadougou"], ["Burma", "Rangoon"], ["Burundi", "Bujumbura"], ["Cambodia", "Phnom Penh"], ["Cameroon", "Yaounde"], ["Canada", "Ottawa"], ["Cape Verde", "Praia"], ["Central African Republic", "Bangui"], ["Chad", "N'Djamena"], ["Chile", "Santiago"], ["China", "Beijing"], ["Colombia", "Bogota"], ["Comoros", "Moroni"], ["Costa Rica", "San Jose"], ["Cote d'Ivoire", "Yamoussoukro"], ["Croatia", "Zagreb"], ["Cuba", "Havana"], ["Cyprus", "Nicosia"], ["Czech Republic", "Prague"], ["Democratic Republic of the Congo", "Kinshasa"], ["Denmark", "Copenhagen"], ["Djibouti", "Djibouti"], ["Dominica", "Roseau"], ["Dominican Republic", "Santo Domingo"], ["East Timor", "Dili"], ["Ecuador", "Quito"], ["Egypt", "Cairo"], ["El Salvador", "San Salvador"], ["Equatorial Guinea", "Malabo"], ["Eritrea", "Asmara"], ["Estonia", "Tallinn"], ["Ethiopia", "Addis Ababa"], ["Federated States of Micronesia", "Palikir"], ["Fiji", "Suva"], ["Finland", "Helsinki"], ["France", "Paris"], ["Gabon", "Libreville"], ["Georgia", "Tbilisi"], ["Germany", "Berlin"], ["Ghana", "Accra"], ["Greece", "Athens"], ["Grenada", "Saint George's"], ["Guatemala", "Guatemala City"], ["Guinea", "Conakry"], ["Guinea-Bissau", "Bissau"], ["Guyana", "Georgetown"], ["Haiti", "Port-au-Prince"], ["Honduras", "Tegucigalpa"], ["Hungary", "Budapest"], ["Iceland", "Reykjavik"], ["India", "New Delhi"], ["Indonesia", "Jakarta"], ["Iran", "Tehran"], ["Iraq", "Baghdad"], ["Ireland", "Dublin"], ["Israel", "Jerusalem"], ["Italy", "Rome"], ["Jamaica", "Kingston"], ["Japan", "Tokyo"], ["Jordan", "Amman"], ["Kazakhstan", "Astana"], ["Kenya", "Nairobi"], ["Kiribati", "Tarawa"], ["Kuwait", "Kuwait City"], ["Kyrgyzstan", "Bishtek"], ["Laos", "Vientiane"], ["Latvia", "Riga"], ["Lebanon", "Beirut"], ["Lesotho", "Maseru"], ["Liberia", "Monrovia"], ["Libya", "Tripoli"], ["Liechtenstein", "Vaduz"], ["Lithuania", "Vilnius"], ["Luxembourg", "Luxembourg"], ["Macedonia", "Skopje"], ["Madagascar", "Antananarivo"], ["Malawi", "Lilongwe"], ["Malaysia", "Kuala Lumpur"], ["Maldives", "Male"], ["Mali", "Bamako"], ["Malta", "Valletta"], ["Marshall Islands", "Majuro"], ["Mauritania", "Nouakchott"], ["Mauritius", "Port Louis"], ["Mexico", "Mexico City"], ["Moldova", "Chisinau"], ["Monaco", "Monaco"], ["Mongolia", "Ulaanbaatar"], ["Morocco", "Rabat"], ["Mozambique", "Maputo"], ["Namibia", "Windhoek"], ["Nauru", "Yaren District"], ["Nepal", "Kathmandu"], ["Netherlands", "Amsterdam"], ["New Zealand", "Wellington"], ["Nicaragua", "Managua"], ["Niger", "Niamey"], ["Nigeria", "Abuja"], ["North Korea", "Pyongyang"], ["Norway", "Oslo"], ["Oman", "Muscat"], ["Pakistan", "Islamabad"], ["Palau", "Koror"], ["Panama", "Panama City"], ["Papua New Guinea", "Port Moresby"], ["Paraguay", "Asuncion"], ["Peru", "Lima"], ["Philippines", "Manila"], ["Poland", "Warsaw"], ["Portugal", "Lisbon"], ["Qatar", "Doha"], ["Republic of the Congo", "Brazzaville"], ["Romania", "Bucharest"], ["Russia", "Moscow"], ["Rwanda", "Kigali"], ["Saint Kitts and Nevis", "Basseterre"], ["Saint Lucia", "Castries"], ["Saint Vincent and the Grenadines", "Kingstown"], ["Samoa", "Apia"], ["San Marino", "San Marino"], ["Sao Tome and Principe", "Sao Tome"], ["Saudi Arabia", "Riyadh"], ["Senegal", "Dakar"], ["Serbia and Montenegro", "Belgrade"], ["Seychelles", "Victoria"], ["Sierra Leone", "Freetown"], ["Singapore", "Singapore"], ["Slovakia", "Bratislava"], ["Slovenia", "Ljubljana"], ["Solomon Islands", "Honiara"], ["Somalia", "Mogadishu"], ["South Africa", "Pretoria"], ["South Korea", "Seoul"], ["Spain", "Madrid"], ["Sri Lanka", "Colombo"], ["Sudan", "Khartoum"], ["Suriname", "Paramaribo"], ["Swaziland", "Mbabana"], ["Sweden", "Stockholm"], ["Switzerland", "Bern"], ["Syria", "Damascus"], ["Taiwan", "Taipei"], ["Tajikistan", "Dushanbe"], ["Tanzania", "Dar es Salaam"], ["Thailand", "Bangkok"], ["The Bahamas", "Nassau"], ["The Gambia", "Banjul"], ["Togo", "Lome"], ["Tonga", "Nuku'alofa"], ["Trinidad and Tobago", "Port-of-Spain"], ["Tunisia", "Tunis"], ["Turkey", "Ankara"], ["Turkmenistan", "Ashgabat"], ["Tuvalu", "Funafuti"], ["Uganda", "Kampala"], ["Ukraine", "Kiev"], ["United Arab Emirates", "Abu Dhabi"], ["United Kingdom", "London"], ["United States", "Washington D.C."], ["Uruguay", "Montevideo"], ["Uzbekistan", "Tashkent"], ["Vanuatu", "Port-Vila"], ["Vatican City", "Vatican City"], ["Venezuela", "Caracas"], ["Vietnam", "Hanoi"], ["Yemen", "Sanaa"], ["Zambia", "Lusaka"], ["Zimbabwe", "Harare"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case10_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case10_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:-----------------|
| Krakatoa |
| Mount St. Helens |
| Mount Vesuvius |
| Eyjafjallajokull |
| Nevado del Ruiz |
| Mount Tambora |
| Mauna Loa |
| Mount Pinatubo |
Input Table 2:
| Col_1 |
|:--------------|
| Colombia |
| United States |
| Philippines |
| Italy |
| Iceland |
| Indonesia |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case45", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case45", "case": "Case45", "label": [["Mount Vesuvius", "Italy"], ["Krakatoa", "Indonesia"], ["Mount St. Helens", "United States"], ["Mount Tambora", "Indonesia"], ["Mauna Loa", "United States"], ["Eyjafjallajokull", "Iceland"], ["Nevado del Ruiz", "Colombia"], ["Mount Pinatubo", "Philippines"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case45_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case45_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:---------------------------------|
| Algeria |
| Angola |
| Benin |
| Botswana |
| Burkina |
| Burundi |
| Cameroon |
| Cape Verde |
| Central African Republic |
| Chad |
| Comoros |
| Congo |
| Congo, Democratic Republic of |
| Djibouti |
| Egypt |
| Equatorial Guinea |
| Eritrea |
| Ethiopia |
| Gabon |
| Gambia |
| Ghana |
| Guinea |
| Guinea-Bissau |
| Ivory Coast |
| Kenya |
| Lesotho |
| Liberia |
| Libya |
| Madagascar |
| Malawi |
| Mali |
| Mauritania |
| Mauritius |
| Morocco |
| Mozambique |
| Namibia |
| Niger |
| Nigeria |
| Rwanda |
| Sao Tome and Principe |
| Senegal |
| Seychelles |
| Sierra Leone |
| Somalia |
| South Africa |
| South Sudan |
| Sudan |
| Swaziland |
| Tanzania |
| Togo |
| Tunisia |
| Uganda |
| Zambia |
| Zimbabwe |
| Afghanistan |
| Bahrain |
| Bangladesh |
| Bhutan |
| Brunei |
| Burma (Myanmar) |
| Cambodia |
| China |
| East Timor |
| India |
| Indonesia |
| Iran |
| Iraq |
| Israel |
| Japan |
| Jordan |
| Kazakhstan |
| Korea, North |
| Korea, South |
| Kuwait |
| Kyrgyzstan |
| Laos |
| Lebanon |
| Malaysia |
| Maldives |
| Mongolia |
| Nepal |
| Oman |
| Pakistan |
| Philippines |
| Qatar |
| Russian Federation |
| Saudi Arabia |
| Singapore |
| Sri Lanka |
| Syria |
| Tajikistan |
| Thailand |
| Turkey |
| Turkmenistan |
| United Arab Emirates |
| Uzbekistan |
| Vietnam |
| Yemen |
| Albania |
| Andorra |
| Armenia |
| Austria |
| Azerbaijan |
| Belarus |
| Belgium |
| Bosnia and Herzegovina |
| Bulgaria |
| Croatia |
| Cyprus |
| Czech Republic |
| Denmark |
| Estonia |
| Finland |
| France |
| Georgia |
| Germany |
| Greece |
| Hungary |
| Iceland |
| Ireland |
| Italy |
| Latvia |
| Liechtenstein |
| Lithuania |
| Luxembourg |
| Macedonia |
| Malta |
| Moldova |
| Monaco |
| Montenegro |
| Netherlands |
| Norway |
| Poland |
| Portugal |
| Romania |
| San Marino |
| Serbia |
| Slovakia |
| Slovenia |
| Spain |
| Sweden |
| Switzerland |
| Ukraine |
| United Kingdom |
| Vatican City |
| Antigua and Barbuda |
| Bahamas |
| Barbados |
| Belize |
| Canada |
| Costa Rica |
| Cuba |
| Dominica |
| Dominican Republic |
| El Salvador |
| Grenada |
| Guatemala |
| Haiti |
| Honduras |
| Jamaica |
| Mexico |
| Nicaragua |
| Panama |
| Saint Kitts and Nevis |
| Saint Lucia |
| Saint Vincent and the Grenadines |
| Trinidad and Tobago |
| United States |
| Australia |
| Fiji |
| Kiribati |
| Marshall Islands |
| Micronesia |
| Nauru |
| New Zealand |
| Palau |
| Papua New Guinea |
| Samoa |
| Solomon Islands |
| Tonga |
| Tuvalu |
| Vanuatu |
| Argentina |
| Bolivia |
| Brazil |
| Chile |
| Colombia |
| Ecuador |
| Guyana |
| Paraguay |
| Peru |
| Suriname |
| Uruguay |
| Venezuela |
Input Table 2:
| Col_1 |
|:--------------|
| Africa |
| Asia |
| Europe |
| North America |
| Oceania |
| South America |
| Antarctica |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case1", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case1", "case": "Case1", "label": [["Algeria", "Africa"], ["Angola", "Africa"], ["Benin", "Africa"], ["Botswana", "Africa"], ["Burkina", "Africa"], ["Burundi", "Africa"], ["Cameroon", "Africa"], ["Cape Verde", "Africa"], ["Central African Republic", "Africa"], ["Chad", "Africa"], ["Comoros", "Africa"], ["Congo", "Africa"], ["Congo, Democratic Republic of", "Africa"], ["Djibouti", "Africa"], ["Egypt", "Africa"], ["Equatorial Guinea", "Africa"], ["Eritrea", "Africa"], ["Ethiopia", "Africa"], ["Gabon", "Africa"], ["Gambia", "Africa"], ["Ghana", "Africa"], ["Guinea", "Africa"], ["Guinea-Bissau", "Africa"], ["Ivory Coast", "Africa"], ["Kenya", "Africa"], ["Lesotho", "Africa"], ["Liberia", "Africa"], ["Libya", "Africa"], ["Madagascar", "Africa"], ["Malawi", "Africa"], ["Mali", "Africa"], ["Mauritania", "Africa"], ["Mauritius", "Africa"], ["Morocco", "Africa"], ["Mozambique", "Africa"], ["Namibia", "Africa"], ["Niger", "Africa"], ["Nigeria", "Africa"], ["Rwanda", "Africa"], ["Sao Tome and Principe", "Africa"], ["Senegal", "Africa"], ["Seychelles", "Africa"], ["Sierra Leone", "Africa"], ["Somalia", "Africa"], ["South Africa", "Africa"], ["South Sudan", "Africa"], ["Sudan", "Africa"], ["Swaziland", "Africa"], ["Tanzania", "Africa"], ["Togo", "Africa"], ["Tunisia", "Africa"], ["Uganda", "Africa"], ["Zambia", "Africa"], ["Zimbabwe", "Africa"], ["Afghanistan", "Asia"], ["Bahrain", "Asia"], ["Bangladesh", "Asia"], ["Bhutan", "Asia"], ["Brunei", "Asia"], ["Burma (Myanmar)", "Asia"], ["Cambodia", "Asia"], ["China", "Asia"], ["East Timor", "Asia"], ["India", "Asia"], ["Indonesia", "Asia"], ["Iran", "Asia"], ["Iraq", "Asia"], ["Israel", "Asia"], ["Japan", "Asia"], ["Jordan", "Asia"], ["Kazakhstan", "Asia"], ["Korea, North", "Asia"], ["Korea, South", "Asia"], ["Kuwait", "Asia"], ["Kyrgyzstan", "Asia"], ["Laos", "Asia"], ["Lebanon", "Asia"], ["Malaysia", "Asia"], ["Maldives", "Asia"], ["Mongolia", "Asia"], ["Nepal", "Asia"], ["Oman", "Asia"], ["Pakistan", "Asia"], ["Philippines", "Asia"], ["Qatar", "Asia"], ["Russian Federation", "Asia"], ["Saudi Arabia", "Asia"], ["Singapore", "Asia"], ["Sri Lanka", "Asia"], ["Syria", "Asia"], ["Tajikistan", "Asia"], ["Thailand", "Asia"], ["Turkey", "Asia"], ["Turkmenistan", "Asia"], ["United Arab Emirates", "Asia"], ["Uzbekistan", "Asia"], ["Vietnam", "Asia"], ["Yemen", "Asia"], ["Albania", "Europe"], ["Andorra", "Europe"], ["Armenia", "Europe"], ["Austria", "Europe"], ["Azerbaijan", "Europe"], ["Belarus", "Europe"], ["Belgium", "Europe"], ["Bosnia and Herzegovina", "Europe"], ["Bulgaria", "Europe"], ["Croatia", "Europe"], ["Cyprus", "Europe"], ["Czech Republic", "Europe"], ["Denmark", "Europe"], ["Estonia", "Europe"], ["Finland", "Europe"], ["France", "Europe"], ["Georgia", "Europe"], ["Germany", "Europe"], ["Greece", "Europe"], ["Hungary", "Europe"], ["Iceland", "Europe"], ["Ireland", "Europe"], ["Italy", "Europe"], ["Latvia", "Europe"], ["Liechtenstein", "Europe"], ["Lithuania", "Europe"], ["Luxembourg", "Europe"], ["Macedonia", "Europe"], ["Malta", "Europe"], ["Moldova", "Europe"], ["Monaco", "Europe"], ["Montenegro", "Europe"], ["Netherlands", "Europe"], ["Norway", "Europe"], ["Poland", "Europe"], ["Portugal", "Europe"], ["Romania", "Europe"], ["San Marino", "Europe"], ["Serbia", "Europe"], ["Slovakia", "Europe"], ["Slovenia", "Europe"], ["Spain", "Europe"], ["Sweden", "Europe"], ["Switzerland", "Europe"], ["Ukraine", "Europe"], ["United Kingdom", "Europe"], ["Vatican City", "Europe"], ["Antigua and Barbuda", "North America"], ["Bahamas", "North America"], ["Barbados", "North America"], ["Belize", "North America"], ["Canada", "North America"], ["Costa Rica", "North America"], ["Cuba", "North America"], ["Dominica", "North America"], ["Dominican Republic", "North America"], ["El Salvador", "North America"], ["Grenada", "North America"], ["Guatemala", "North America"], ["Haiti", "North America"], ["Honduras", "North America"], ["Jamaica", "North America"], ["Mexico", "North America"], ["Nicaragua", "North America"], ["Panama", "North America"], ["Saint Kitts and Nevis", "North America"], ["Saint Lucia", "North America"], ["Saint Vincent and the Grenadines", "North America"], ["Trinidad and Tobago", "North America"], ["United States", "North America"], ["Australia", "Oceania"], ["Fiji", "Oceania"], ["Kiribati", "Oceania"], ["Marshall Islands", "Oceania"], ["Micronesia", "Oceania"], ["Nauru", "Oceania"], ["New Zealand", "Oceania"], ["Palau", "Oceania"], ["Papua New Guinea", "Oceania"], ["Samoa", "Oceania"], ["Solomon Islands", "Oceania"], ["Tonga", "Oceania"], ["Tuvalu", "Oceania"], ["Vanuatu", "Oceania"], ["Argentina", "South America"], ["Bolivia", "South America"], ["Brazil", "South America"], ["Chile", "South America"], ["Colombia", "South America"], ["Ecuador", "South America"], ["Guyana", "South America"], ["Paraguay", "South America"], ["Peru", "South America"], ["Suriname", "South America"], ["Uruguay", "South America"], ["Venezuela", "South America"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case1_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case1_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:----------|
| Miranda |
| Tethys |
| Moon |
| Io |
| Enceladus |
| Mimas |
Input Table 2:
| Col_1 |
|:--------|
| Earth |
| Jupiter |
| Saturn |
| Uranus |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case49", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case49", "case": "Case49", "label": [["Mimas", "Saturn"], ["Enceladus", "Saturn"], ["Miranda", "Uranus"], ["Tethys", "Saturn"], ["Io", "Jupiter"], ["Moon", "Earth"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case49_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case49_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:----------------|
| Lingala |
| Hebrew |
| Bashkir |
| Sangro |
| Abkhazian |
| Mongolian |
| Moldavian |
| Zulu |
| Shona |
| Swedish |
| Turkish |
| Ukrainian |
| Kannada |
| Catalan |
| Malay |
| Tegulu |
| Sudanese |
| Armenian |
| Swahili |
| Urdu |
| Romanian |
| Twi |
| Afar |
| Yiddish |
| Macedonian |
| Breton |
| Japanese |
| Estonian |
| Sindhi |
| Bengali, Bangla |
| Gujarati |
| Icelandic |
| Tatar |
| Singhalese |
| Laothian |
| Fiji |
| Tibetan |
| Uzbek |
| Persian |
| German |
| Bhutani |
| Kurdish |
| Serbo-Croatian |
| Azerbaijani |
| Bihari |
| French |
| Greenlandic |
| Maltese |
| Indonesian |
| Esperanto |
| Tagalog |
| Turkmen |
| Nepali |
| Georgian |
| Basque |
| Malayalam |
| Lithuanian |
| Cambodian |
| Korean |
| Assamese |
| Tamil |
| Chinese |
| Vietnamese |
| Czech |
| Galician |
| Somali |
| Dutch |
| Sanskrit |
| Slovak |
| Russian |
| Serbian |
| Marathi |
| Rhaeto-Romance |
| Kirundi |
| Kirghiz |
| Samoan |
| Kinyarwanda |
| Tajik |
| Arabic |
| Portuguese |
| Hindi |
| Spanish |
| Thai |
| Norwegian |
| Tonga |
| Malagasy |
| Tigrinya |
| Siswati |
| Occitan |
| Interlingue |
| Irish |
| Guarani |
| Kazakh |
| Burmese |
| Inupiak |
| Javanese |
| Byelorussian |
| Wolof |
| Latin |
| Bislama |
| Setswana |
| Nauru |
| Kashmiri |
| Oriya |
| Italian |
| Corsican |
| Tsonga |
| Afrikaans |
| Yoruba |
| Slovenian |
| Faeroese |
| Interlingua |
| Maori |
| Finnish |
| Volapuk |
| Aymara |
| Albanian |
| Polish |
| Xhosa |
| Bulgarian |
| Sesotho |
| Welsh |
| Hausa |
| Croatian |
| Quechua |
| English |
| Danish |
| Frisian |
| Hungarian |
| Amharic |
| Greek |
| Punjabi |
Input Table 2:
| Col_1 |
|:--------|
| HI |
| PT |
| HR |
| HU |
| HY |
| YO |
| IA |
| IE |
| AA |
| AB |
| IK |
| QU |
| AF |
| IN |
| IS |
| IT |
| AM |
| ZH |
| IW |
| AR |
| AS |
| JA |
| AY |
| AZ |
| RM |
| ZU |
| RN |
| RO |
| BA |
| JI |
| BE |
| RU |
| BG |
| RW |
| BH |
| BI |
| BN |
| JW |
| BO |
| SA |
| BR |
| SD |
| SG |
| SH |
| KA |
| SI |
| SK |
| SL |
| SM |
| SN |
| SO |
| SQ |
| CA |
| SR |
| KK |
| SS |
| KL |
| ST |
| KM |
| SU |
| KN |
| SV |
| KO |
| SW |
| KS |
| KU |
| CO |
| TA |
| KY |
| CS |
| TE |
| TG |
| TH |
| LA |
| TI |
| CY |
| TK |
| TL |
| TN |
| TO |
| DA |
| TR |
| TS |
| TT |
| DE |
| LN |
| LO |
| TW |
| LT |
| DZ |
| UK |
| MG |
| MI |
| UR |
| MK |
| ML |
| MN |
| MO |
| MR |
| UZ |
| MS |
| EL |
| MT |
| EN |
| EO |
| MY |
| ES |
| ET |
| EU |
| NA |
| VI |
| NE |
| VO |
| FA |
| NL |
| NO |
| FI |
| FJ |
| FO |
| FR |
| FY |
| OC |
| WO |
| GA |
| OR |
| GL |
| GN |
| GU |
| XH |
| PA |
| HA |
| PL |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case24", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case24", "case": "Case24", "label": [["Abkhazian", "AB"], ["Afar", "AA"], ["Afrikaans", "AF"], ["Albanian", "SQ"], ["Amharic", "AM"], ["Arabic", "AR"], ["Armenian", "HY"], ["Assamese", "AS"], ["Aymara", "AY"], ["Azerbaijani", "AZ"], ["Bashkir", "BA"], ["Basque", "EU"], ["Bengali, Bangla", "BN"], ["Bhutani", "DZ"], ["Bihari", "BH"], ["Bislama", "BI"], ["Breton", "BR"], ["Bulgarian", "BG"], ["Burmese", "MY"], ["Byelorussian", "BE"], ["Cambodian", "KM"], ["Catalan", "CA"], ["Chinese", "ZH"], ["Corsican", "CO"], ["Croatian", "HR"], ["Czech", "CS"], ["Danish", "DA"], ["Dutch", "NL"], ["English", "EN"], ["Esperanto", "EO"], ["Estonian", "ET"], ["Faeroese", "FO"], ["Fiji", "FJ"], ["Finnish", "FI"], ["French", "FR"], ["Frisian", "FY"], ["Galician", "GL"], ["Georgian", "KA"], ["German", "DE"], ["Greek", "EL"], ["Greenlandic", "KL"], ["Guarani", "GN"], ["Gujarati", "GU"], ["Hausa", "HA"], ["Hebrew", "IW"], ["Hindi", "HI"], ["Hungarian", "HU"], ["Icelandic", "IS"], ["Indonesian", "IN"], ["Interlingua", "IA"], ["Interlingue", "IE"], ["Inupiak", "IK"], ["Irish", "GA"], ["Italian", "IT"], ["Japanese", "JA"], ["Javanese", "JW"], ["Kannada", "KN"], ["Kashmiri", "KS"], ["Kazakh", "KK"], ["Kinyarwanda", "RW"], ["Kirghiz", "KY"], ["Kirundi", "RN"], ["Korean", "KO"], ["Kurdish", "KU"], ["Laothian", "LO"], ["Latin", "LA"], ["Lingala", "LN"], ["Lithuanian", "LT"], ["Macedonian", "MK"], ["Malagasy", "MG"], ["Malay", "MS"], ["Malayalam", "ML"], ["Maltese", "MT"], ["Maori", "MI"], ["Marathi", "MR"], ["Moldavian", "MO"], ["Mongolian", "MN"], ["Nauru", NaN], ["Nepali", "NE"], ["Norwegian", "NO"], ["Occitan", "OC"], ["Oriya", "OR"], ["Persian", "FA"], ["Polish", "PL"], ["Portuguese", "PT"], ["Punjabi", "PA"], ["Quechua", "QU"], ["Rhaeto-Romance", "RM"], ["Romanian", "RO"], ["Russian", "RU"], ["Samoan", "SM"], ["Sangro", "SG"], ["Sanskrit", "SA"], ["Serbian", "SR"], ["Serbo-Croatian", "SH"], ["Sesotho", "ST"], ["Setswana", "TN"], ["Shona", "SN"], ["Sindhi", "SD"], ["Singhalese", "SI"], ["Siswati", "SS"], ["Slovak", "SK"], ["Slovenian", "SL"], ["Somali", "SO"], ["Spanish", "ES"], ["Sudanese", "SU"], ["Swahili", "SW"], ["Swedish", "SV"], ["Tagalog", "TL"], ["Tajik", "TG"], ["Tamil", "TA"], ["Tatar", "TT"], ["Tegulu", "TE"], ["Thai", "TH"], ["Tibetan", "BO"], ["Tigrinya", "TI"], ["Tonga", "TO"], ["Tsonga", "TS"], ["Turkish", "TR"], ["Turkmen", "TK"], ["Twi", "TW"], ["Ukrainian", "UK"], ["Urdu", "UR"], ["Uzbek", "UZ"], ["Vietnamese", "VI"], ["Volapuk", "VO"], ["Welsh", "CY"], ["Wolof", "WO"], ["Xhosa", "XH"], ["Yiddish", "JI"], ["Yoruba", "YO"], ["Zulu", "ZU"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case24_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case24_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:------------|
| Brampton |
| Winnipeg |
| Vancouver |
| Calgary |
| Edmonton |
| Mississauga |
| Hamilton |
| Ottawa |
| Montreal |
| Toronto |
Input Table 2:
| Col_1 |
|:-----------------|
| Manitoba |
| Alberta |
| Quebec |
| British Columbia |
| Ontario |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case41", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case41", "case": "Case41", "label": [["Toronto", "Ontario"], ["Montreal", "Quebec"], ["Calgary", "Alberta"], ["Ottawa", "Ontario"], ["Edmonton", "Alberta"], ["Mississauga", "Ontario"], ["Winnipeg", "Manitoba"], ["Vancouver", "British Columbia"], ["Brampton", "Ontario"], ["Hamilton", "Ontario"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case41_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case41_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:--------------------------|
| Martin Van Buren |
| George Washington |
| John Adams |
| John Quincy Adams |
| Dwight D. Eisenhower |
| Herbert Hoover |
| James A. Garfield |
| Bill Clinton |
| John F. Kennedy |
| Gerald R. Ford |
| George W. Bush |
| Franklin D. Roosevelt |
| Jimmy Carter |
| James Madison |
| Andrew Johnson |
| Rutherford Birchard Hayes |
| Ulysses S. Grant |
| Andrew Jackson |
| Ronald Reagan |
| Grover Cleveland |
| Chester A. Arthur |
| Warren G. Harding |
| Benjamin Harrison |
| Richard M. Nixon |
| Millard Fillmore |
| Franklin Pierce |
| Abraham Lincoln |
| Barack Obama |
| Harry S. Truman |
| William McKinley |
| Theodore Roosevelt |
| Lyndon B. Johnson |
| George Bush |
Input Table 2:
| Col_1 |
|:----------------------------------------------------|
| Ida Saxton McKinley |
| Mamie Doud Eisenhower |
| Eleanor Roosevelt |
| Lady Bird Johnson |
| Caroline Lavinia Scott Harrison; Mary Lord Harrison |
| Laura Bush |
| Mary Todd Lincoln |
| Frances Folsom Cleveland |
| Ellen Lewis Herndon Arthur |
| Eliza McCardle Johnson |
| Jane M. Pierce |
| Nancy Reagan |
| Lucy Webb Hayes |
| Edith Kermit Carow Roosevelt |
| Lou Henry Hoover |
| Jacqueline Kennedy Onassis |
| Michelle Obama |
| Pat Nixon |
| Abigail Adams |
| Hannah Hoes Van Buren |
| Bess Wallace Truman |
| Rachel Jackson |
| Betty Ford |
| Dolley Madison |
| Martha Washington |
| Florence Kling Harding |
| Hillary Rodham Clinton |
| Abigail Powers Fillmore |
| Lucretia Rudolph Garfield |
| Rosalynn Carter |
| Barbara Bush |
| Louisa Catherine Adams |
| Julia Dent Grant |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case46", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case46", "case": "Case46", "label": [["George Washington", "Martha Washington"], ["John Adams", "Abigail Adams"], ["James Madison", "Dolley Madison"], ["John Quincy Adams", "Louisa Catherine Adams"], ["Andrew Jackson", "Rachel Jackson"], ["Martin Van Buren", "Hannah Hoes Van Buren"], ["Millard Fillmore", "Abigail Powers Fillmore"], ["Franklin Pierce", "Jane M. Pierce"], ["Abraham Lincoln", "Mary Todd Lincoln"], ["Andrew Johnson", "Eliza McCardle Johnson"], ["Ulysses S. Grant", "Julia Dent Grant"], ["Rutherford Birchard Hayes", "Lucy Webb Hayes"], ["James A. Garfield", "Lucretia Rudolph Garfield"], ["Chester A. Arthur", "Ellen Lewis Herndon Arthur"], ["Grover Cleveland", "Frances Folsom Cleveland"], ["Benjamin Harrison", "Caroline Lavinia Scott Harrison; Mary Lord Harrison"], ["Grover Cleveland", "Frances Folsom Cleveland"], ["William McKinley", "Ida Saxton McKinley"], ["Theodore Roosevelt", "Edith Kermit Carow Roosevelt"], ["Warren G. Harding", "Florence Kling Harding"], ["Herbert Hoover", "Lou Henry Hoover"], ["Franklin D. Roosevelt", "Eleanor Roosevelt"], ["Harry S. Truman", "Bess Wallace Truman"], ["Dwight D. Eisenhower", "Mamie Doud Eisenhower"], ["John F. Kennedy", "Jacqueline Kennedy Onassis"], ["Lyndon B. Johnson", "Lady Bird Johnson"], ["Richard M. Nixon", "Pat Nixon"], ["Gerald R. Ford", "Betty Ford"], ["Jimmy Carter", "Rosalynn Carter"], ["Ronald Reagan", "Nancy Reagan"], ["George Bush", "Barbara Bush"], ["Bill Clinton", "Hillary Rodham Clinton"], ["George W. Bush", "Laura Bush"], ["Barack Obama", "Michelle Obama"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case46_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case46_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:--------|
| Ac |
| Ag |
| Al |
| Am |
| Ar |
| As |
| At |
| Au |
| B |
| Ba |
| Be |
| Bh |
| Bi |
| Bk |
| Br |
| C |
| Ca |
| Cd |
| Ce |
| Cf |
| Cl |
| Cm |
| Co |
| Cr |
| Cs |
| Cu |
| Db |
| Ds |
| Dy |
| Er |
| Es |
| Eu |
| F |
| Fe |
| Fm |
| Fr |
| Ga |
| Gd |
| Ge |
| H |
| He |
| Hf |
| Hg |
| Ho |
| Hs |
| I |
| In |
| Ir |
| K |
| Kr |
| La |
| Li |
| Lr |
| Lu |
| Md |
| Mg |
| Mn |
| Mo |
| Mt |
| N |
| Na |
| Nb |
| Nd |
| Ne |
| Ni |
| No |
| Np |
| O |
| Os |
| P |
| Pa |
| Pb |
| Pd |
| Pm |
| Po |
| Pr |
| Pt |
| Pu |
| Ra |
| Rb |
| Re |
| Rf |
| Rg |
| Rh |
| Rn |
| Ru |
| S |
| Sb |
| Sc |
| Se |
| Sg |
| Si |
| Sm |
| Sn |
| Sr |
| Ta |
| Tb |
| Tc |
| Te |
| Th |
| Ti |
| Tl |
| Tm |
| U |
| Uub |
| Uuh |
| Uuo |
| Uup |
| Uuq |
| Uus |
| Uut |
| V |
| W |
| Xe |
| Y |
| Yb |
| Zn |
| Zr |
Input Table 2:
| Col_1 |
|:--------------|
| Actinium |
| Silver |
| Aluminium |
| Americium |
| Argon |
| Arsenic |
| Astatine |
| Gold |
| Boron |
| Barium |
| Beryllium |
| Bohrium |
| Bismuth |
| Berkelium |
| Bromine |
| Carbon |
| Calcium |
| Cadmium |
| Cerium |
| Californium |
| Chlorine |
| Curium |
| Cobalt |
| Chromium |
| Caesium |
| Copper |
| Dubnium |
| Darmstadtium |
| Dysprosium |
| Erbium |
| Einsteinium |
| Europium |
| Fluorine |
| Iron |
| Fermium |
| Francium |
| Gallium |
| Gadolinium |
| Germanium |
| Hydrogen |
| Helium |
| Hafnium |
| Mercury |
| Holmium |
| Hassium |
| Iodine |
| Indium |
| Iridium |
| Potassium |
| Krypton |
| Lanthanum |
| Lithium |
| Lawrencium |
| Lutetium |
| Mendelevium |
| Magnesium |
| Manganese |
| Molybdenum |
| Meitnerium |
| Nitrogen |
| Sodium |
| Niobium |
| Neodymium |
| Neon |
| Nickel |
| Nobelium |
| Neptunium |
| Oxygen |
| Osmium |
| Phosphorus |
| Protactinium |
| Lead |
| Palladium |
| Promethium |
| Polonium |
| Praseodymium |
| Platinum |
| Plutonium |
| Radium |
| Rubidium |
| Rhenium |
| Rutherfordium |
| Roentgenium |
| Rhodium |
| Radon |
| Ruthenium |
| Sulphur |
| Antimony |
| Scandium |
| Selenium |
| Seaborgium |
| Silicon |
| Samarium |
| Tin |
| Strontium |
| Tantalum |
| Terbium |
| Technetium |
| Tellurium |
| Thorium |
| Titanium |
| Thallium |
| Thulium |
| Uranium |
| Ununbium |
| Ununhexium |
| Ununoctium |
| Ununpentium |
| Ununquadium |
| Ununseptium |
| Ununtrium |
| Vanadium |
| Tungsten |
| Xenon |
| Yttrium |
| Ytterbium |
| Zinc |
| Zirconium |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case3", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case3", "case": "Case3", "label": [["Ac", "Actinium"], ["Ag", "Silver"], ["Al", "Aluminium"], ["Am", "Americium"], ["Ar", "Argon"], ["As", "Arsenic"], ["At", "Astatine"], ["Au", "Gold"], ["B", "Boron"], ["Ba", "Barium"], ["Be", "Beryllium"], ["Bh", "Bohrium"], ["Bi", "Bismuth"], ["Bk", "Berkelium"], ["Br", "Bromine"], ["C", "Carbon"], ["Ca", "Calcium"], ["Cd", "Cadmium"], ["Ce", "Cerium"], ["Cf", "Californium"], ["Cl", "Chlorine"], ["Cm", "Curium"], ["Co", "Cobalt"], ["Cr", "Chromium"], ["Cs", "Caesium"], ["Cu", "Copper"], ["Db", "Dubnium"], ["Ds", "Darmstadtium"], ["Dy", "Dysprosium"], ["Er", "Erbium"], ["Es", "Einsteinium"], ["Eu", "Europium"], ["F", "Fluorine"], ["Fe", "Iron"], ["Fm", "Fermium"], ["Fr", "Francium"], ["Ga", "Gallium"], ["Gd", "Gadolinium"], ["Ge", "Germanium"], ["H", "Hydrogen"], ["He", "Helium"], ["Hf", "Hafnium"], ["Hg", "Mercury"], ["Ho", "Holmium"], ["Hs", "Hassium"], ["I", "Iodine"], ["In", "Indium"], ["Ir", "Iridium"], ["K", "Potassium"], ["Kr", "Krypton"], ["La", "Lanthanum"], ["Li", "Lithium"], ["Lr", "Lawrencium"], ["Lu", "Lutetium"], ["Md", "Mendelevium"], ["Mg", "Magnesium"], ["Mn", "Manganese"], ["Mo", "Molybdenum"], ["Mt", "Meitnerium"], ["N", "Nitrogen"], ["Na", "Sodium"], ["Nb", "Niobium"], ["Nd", "Neodymium"], ["Ne", "Neon"], ["Ni", "Nickel"], ["No", "Nobelium"], ["Np", "Neptunium"], ["O", "Oxygen"], ["Os", "Osmium"], ["P", "Phosphorus"], ["Pa", "Protactinium"], ["Pb", "Lead"], ["Pd", "Palladium"], ["Pm", "Promethium"], ["Po", "Polonium"], ["Pr", "Praseodymium"], ["Pt", "Platinum"], ["Pu", "Plutonium"], ["Ra", "Radium"], ["Rb", "Rubidium"], ["Re", "Rhenium"], ["Rf", "Rutherfordium"], ["Rg", "Roentgenium"], ["Rh", "Rhodium"], ["Rn", "Radon"], ["Ru", "Ruthenium"], ["S", "Sulphur"], ["Sb", "Antimony"], ["Sc", "Scandium"], ["Se", "Selenium"], ["Sg", "Seaborgium"], ["Si", "Silicon"], ["Sm", "Samarium"], ["Sn", "Tin"], ["Sr", "Strontium"], ["Ta", "Tantalum"], ["Tb", "Terbium"], ["Tc", "Technetium"], ["Te", "Tellurium"], ["Th", "Thorium"], ["Ti", "Titanium"], ["Tl", "Thallium"], ["Tm", "Thulium"], ["U", "Uranium"], ["Uub", "Ununbium"], ["Uuh", "Ununhexium"], ["Uuo", "Ununoctium"], ["Uup", "Ununpentium"], ["Uuq", "Ununquadium"], ["Uus", "Ununseptium"], ["Uut", "Ununtrium"], ["V", "Vanadium"], ["W", "Tungsten"], ["Xe", "Xenon"], ["Y", "Yttrium"], ["Yb", "Ytterbium"], ["Zn", "Zinc"], ["Zr", "Zirconium"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case3_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case3_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:-------------------------------|
| August Wilson Theatre |
| St James Theatre |
| Broadway Theatre |
| Belasco Theatre |
| Shubert Theatre |
| Bernard B Jacobs Theatre |
| Gershwin Theatre |
| Nederlander Theatre |
| Al Hirschfeld Theatre |
| Walter Kerr Theatre |
| Cadillac Winter Garden Theatre |
| Circle In The Square Theatre |
| Music Box Theatre |
| Ethel Barrymore Theatre |
| Gerald Schoenfeld Theatre |
| Longacre Theatre |
| Samuel J Friedman Theatre |
| Vivian Beaumont Theatre |
| New Amsterdam Theatre |
| Lunt Fontanne Theatre |
| Booth Theatre |
| Cort Theatre |
| Minskoff Theatre |
| Palace Theatre |
| American Airlines Theatre |
| Lyric Theatre |
| John Golden Theatre |
| Helen Hayes Theatre |
| Imperial Theatre |
| Lyceum Theatre |
| Broadhurst Theatre |
| Neil Simon Theatre |
| Majestic Theatre |
| Marquis Theatre |
| Richard Rodgers Theatre |
| Brooks Atkinson Theatre |
| Ambassador Theatre |
Input Table 2:
| Col_1 |
|:--------------------------------|
| Circle in the Square |
| Manhattan; Theatre; Club |
| Nederlander Organization |
| Disney; Theatrical; Productions |
| Lincoln Center Theater |
| Shubert Organization |
| Second Stage |
| Clear Channel |
| Jujamcyn Theaters |
| Roundabout Theatre; Company |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case48", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case48", "case": "Case48", "label": [["Al Hirschfeld Theatre", "Jujamcyn Theaters"], ["Ambassador Theatre", "Shubert Organization"], ["American Airlines Theatre", "Roundabout Theatre; Company"], ["August Wilson Theatre", "Jujamcyn Theaters"], ["Belasco Theatre", "Shubert Organization"], ["Bernard B Jacobs Theatre", "Shubert Organization"], ["Booth Theatre", "Shubert Organization"], ["Broadhurst Theatre", "Shubert Organization"], ["Broadway Theatre", "Shubert Organization"], ["Brooks Atkinson Theatre", "Nederlander Organization"], ["Cadillac Winter Garden Theatre", "Shubert Organization"], ["Circle In The Square Theatre", "Circle in the Square"], ["Cort Theatre", "Shubert Organization"], ["Ethel Barrymore Theatre", "Shubert Organization"], ["Gerald Schoenfeld Theatre", "Shubert Organization"], ["Gershwin Theatre", "Nederlander Organization"], ["Helen Hayes Theatre", "Second Stage"], ["Imperial Theatre", "Shubert Organization"], ["John Golden Theatre", "Shubert Organization"], ["Longacre Theatre", "Shubert Organization"], ["Lunt Fontanne Theatre", "Nederlander Organization"], ["Lyceum Theatre", "Shubert Organization"], ["Lyric Theatre", "Clear Channel"], ["Majestic Theatre", "Shubert Organization"], ["Marquis Theatre", "Nederlander Organization"], ["Minskoff Theatre", "Nederlander Organization"], ["Music Box Theatre", "Shubert Organization"], ["Nederlander Theatre", "Nederlander Organization"], ["Neil Simon Theatre", "Nederlander Organization"], ["New Amsterdam Theatre", "Disney; Theatrical; Productions"], ["Palace Theatre", "Nederlander Organization"], ["Richard Rodgers Theatre", "Nederlander Organization"], ["Samuel J Friedman Theatre", "Manhattan; Theatre; Club"], ["Shubert Theatre", "Shubert Organization"], ["St James Theatre", "Jujamcyn Theaters"], ["Vivian Beaumont Theatre", "Lincoln Center Theater"], ["Walter Kerr Theatre", "Jujamcyn Theaters"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case48_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case48_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:--------------------|
| Larry Page |
| David Koch |
| Amancio Ortega |
| Charles Koch |
| Carlos Slim Helu |
| Warren Buffett |
| Jeff Bezos |
| Stefan Persson |
| Larry Ellison |
| Li Ka-shing |
| Jim Walton |
| Christy Walton |
| S. Robson Walton |
| Michael Bloomberg |
| Liliane Bettencourt |
| Sheldon Adelson |
| Alice Walton |
| Bernard Arnault |
| Bill Gates |
| Sergey Brin |
Input Table 2:
| Col_1 |
|:-------------------|
| Google |
| L’Oreal |
| LVMH |
| Diversified |
| H&M |
| Telecom |
| Retail |
| Walmart |
| Casinos |
| Bloomberg LP |
| Microsoft |
| Oracle |
| Amazon |
| Berkshire Hathaway |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case37", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case37", "case": "Case37", "label": [["Bill Gates", "Microsoft"], ["Carlos Slim Helu", "Telecom"], ["Amancio Ortega", "Retail"], ["Warren Buffett", "Berkshire Hathaway"], ["Larry Ellison", "Oracle"], ["Charles Koch", "Diversified"], ["David Koch", "Diversified"], ["Sheldon Adelson", "Casinos"], ["Christy Walton", "Walmart"], ["Jim Walton", "Walmart"], ["Liliane Bettencourt", "L\u2019Oreal"], ["Stefan Persson", "H&M"], ["Alice Walton", "Walmart"], ["S. Robson Walton", "Walmart"], ["Bernard Arnault", "LVMH"], ["Michael Bloomberg", "Bloomberg LP"], ["Larry Page", "Google"], ["Jeff Bezos", "Amazon"], ["Sergey Brin", "Google"], ["Li Ka-shing", "Diversified"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case37_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case37_groundtruth.txt"]} | semantic-join | SEMA-join |
You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships.
No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.
Input Table 1:
| Col_0 |
|:-------------------------------------------|
| BAIC |
| Chrysler |
| Suzuki |
| China National Heavy Duty Truck |
| AvtoVAZ |
| Shannxi |
| Hyundai |
| Tata |
| PSA |
| Chery |
| Mazda |
| Navistar |
| Paccar |
| Sichuan Nanjun |
| Daimler AG |
| South East (Fujian) |
| Guangzhou Auto Industry |
| Chongqing Lifan Motor Co |
| Toyota |
| Tangjun Ou Ling |
| Brilliance |
| GAZ |
| Volkswagen |
| Volvo |
| Hunan Jiangnan Automobile Manufacturing Co |
| Hebei Zhongxing |
| BYD |
| Honda |
| SAIC |
| Fuji |
| UAZ |
| Dongfeng Motor |
| Xiamen King Long |
| Proton |
| Porsche |
| Isuzu |
| Ashok Leyland |
| Changan |
| Renault |
| Geely |
| GM |
| Mitsubishi |
| BMW |
| JAC |
| Mahindra |
| Great Wall |
| Fiat |
| Ford |
| FAW |
| Nissan |
Input Table 2:
| Col_1 |
|:--------------|
| South Korea |
| Sweden |
| United States |
| Japan |
| China |
| Italy |
| Malaysia |
| France |
| Germany |
| India |
| Russia |
Joined Table:
| {"task": "semantic-join", "version": "1.0_sample1000_markdown", "tag": "1.0_sample1000_markdown", "note": "", "dataset": "SEMA-join", "test_case": "Case34", "case_path": "$MMTU_HOME/data/semantic-join/sample1000-3shots/SEMA-join/Case34", "case": "Case34", "label": [["Toyota", "Japan"], ["GM", "United States"], ["Volkswagen", "Germany"], ["Hyundai", "South Korea"], ["Ford", "United States"], ["Nissan", "Japan"], ["Honda", "Japan"], ["PSA", "France"], ["Suzuki", "Japan"], ["Renault", "France"], ["Chrysler", "United States"], ["Daimler AG", "Germany"], ["Fiat", "Italy"], ["BMW", "Germany"], ["SAIC", "China"], ["Tata", "India"], ["Mazda", "Japan"], ["Dongfeng Motor", "China"], ["Mitsubishi", "Japan"], ["Changan", "China"], ["Geely", "China"], ["Fuji", "Japan"], ["BAIC", "China"], ["FAW", "China"], ["Great Wall", "China"], ["Mahindra", "India"], ["Isuzu", "Japan"], ["Chery", "China"], ["AvtoVAZ", "Russia"], ["Brilliance", "China"], ["JAC", "China"], ["BYD", "China"], ["Chongqing Lifan Motor Co", "China"], ["Volvo", "Sweden"], ["Proton", "Malaysia"], ["China National Heavy Duty Truck", "China"], ["Paccar", "United States"], ["GAZ", "Russia"], ["Ashok Leyland", "India"], ["Hunan Jiangnan Automobile Manufacturing Co", "China"], ["Guangzhou Auto Industry", "China"], ["Shannxi", "China"], ["Porsche", "Germany"], ["South East (Fujian)", "China"], ["Navistar", "United States"], ["Xiamen King Long", "China"], ["UAZ", "Russia"], ["Tangjun Ou Ling", "China"], ["Hebei Zhongxing", "China"], ["Sichuan Nanjun", "China"]], "sources": ["Semantic-Join/Semantic-join-Benchmark/Case34_input.txt", "Semantic-Join/Semantic-join-Benchmark/Case34_groundtruth.txt"]} | semantic-join | SEMA-join |
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Dataset Card for MMTU
Dataset Summary
|🛠️GitHub |🏆Leaderboard|📖 Paper |
MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark by Junjie Xing, Yeye He, Mengyu Zhou, Haoyu Dong, Shi Han, Lingjiao Chen, Dongmei Zhang, Surajit Chaudhuri, and H. V. Jagadish.
Tables and table-based use cases play a crucial role in many real-world applications, such as spreadsheets, databases, and computational notebooks, which traditionally require expert-level users like data engineers, analysts, and database administrators to operate. Although LLMs have shown remarkable progress in working with tables, comprehensive benchmarking of such capabilities remains limited, often narrowly focusing on tasks like NL-to-SQL and Table-QA, while overlooking the broader spectrum of real-world tasks that professional users face today.
We introduce MMTU, a large-scale benchmark with over 30K questions across 25 real-world table tasks, designed to comprehensively evaluate models ability to understand, reason, and manipulate real tables at the expert-level. These tasks are drawn from decades' worth of computer science research on tabular data, with a focus on complex table tasks faced by professional users. We show that MMTU require a combination of skills -- including table understanding, reasoning, and coding -- that remain challenging for today's frontier models, where even frontier reasoning models like OpenAI o4-mini and DeepSeek R1 score only around 60%, suggesting significant room for improvement. Our evaluation code is available at GitHub.
Dataset Creation
MMTU was developed through the meticulous curation of 52 datasets across 25 task categories, each carefully labeled by computer science researchers, in decades’ worth of research on tabular data from communities such as data management (SIGMOD/VLDB), programming languages (PLDI/POPL), and web data (WWW/WSDM). The benchmark emphasizes real-world, complex table tasks encountered by professional users—tasks that demand advanced skills in table understanding, coding, and reasoning. Plesae see the table below for key statistics of the benchmark.
A complete list of tasks: 'table-transform-by-relationalization', 'table-transform-by-output-schema', 'table-transform-by-output-table', 'Entity matching', 'Schema matching', 'Head value matching', 'data-imputation', 'error-detection', 'list-to-table', 'semantic-join', 'equi-join-detect', 'program-transform-by-example', 'formula-by-context', 'semantic-transform-by-example', 'arithmetic-relationship', 'functional-relationship', 'string-relationship', 'Needle-in-a-haystack-table', 'Needle-in-a-haystack-index', 'NL-2-SQL', 'Table Question Answering', 'Fact Verification', 'Column type annotation', 'Column property annotation', 'Cell entity annotation'.
Leaderboard
| Model Type | Model | MMTU Score |
|---|---|---|
| Reasoning | o4-mini (2024-11-20) | 0.639 ± 0.01 |
| Reasoning | Deepseek-R1 | 0.596 ± 0.01 |
| Chat | Deepseek-V3 | 0.517 ± 0.01 |
| Chat | GPT-4o (2024-11-20) | 0.491 ± 0.01 |
| Chat | Llama-3.3-70B | 0.438 ± 0.01 |
| Chat | Mistral-Large-2411 | 0.430 ± 0.01 |
| Chat | Mistral-Small-2503 | 0.402 ± 0.01 |
| Chat | GPT-4o-mini (2024-07-18) | 0.386 ± 0.01 |
| Chat | Llama-3.1-8B | 0.259 ± 0.01 |
Language
English
Data Structure
Data Fields
- prompt: The prompt presented in the MMTU instance.
- metadata: Supplementary information associated with the MMTU instance, typically used for evaluation purposes.
- task: The specific subtask category within the MMTU framework to which the instance belongs.
- dataset: The original source dataset from which the MMTU instance is derived.
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