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widesearch.jsonl

@@ -92,7 +92,7 @@
 {"instance_id":"ws_en_092","query":"I would like to know about the boy groups and girl groups (no sub-units) that debuted after 2000 (excluding 2000) and before 2025 (including 2025) from the three major entertainment companies in Korea (SM, JYP and YG) and the date and song when they first won the first place in music programs aired by the major radio stations (KBS, MBC, SBS and Mnet). Please provide the date when they first won a music program in the format yyyy-mm-dd, e.g. 2024-01-23. If the corresponding data is not retrieved, please fill in with \"-\".\n\nPlease output the organized data in one Markdown table format.\nThe column headers should be as follows:\nCompany Name, Debut Year, Official Group Name (English), First Win Date, First Win Song\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is:\n```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"officialgroupname(english)\"], \"required\": [\"companyname\", \"debutyear\", \"officialgroupname(english)\", \"firstwinsong\", \"firstwindate\"], \"eval_pipeline\": {\"companyname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"debutyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"officialgroupname(english)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"firstwindate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"firstwinsong\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_093","query":"I want to catch up with the current large language model trends, so I need to quickly learn about them. Please help me compile a list of LLM-related papers officially published by Doubao Seed Team from Bytedance and DeepSeek from 2023 to the first half of 2025. The list should include: Publication date (in yyyy-mm-dd format, e.g. 2024-02-01), Paper title (in original English as published), and authors. By the way, I only want papers published independently by Doubao Seed Team from Bytedance and the DeepSeek team, not collaborative papers.\n\nPlease output the compiled data in Markdown table format.\n The column headers should be:\nCompany Name, Publication Date, Paper Title, Authors.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is:\n```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"papertitle\"], \"required\": [\"companyname\", \"publicationdate\", \"papertitle\", \"authors\"], \"eval_pipeline\": {\"publicationdate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"papertitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"companyname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"authors\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_094","query":"I'm compiling a list of the top three box office films released in South Korea for each year from Jan 2010 to Dec 2024. Please organize the top three films each year based on cumulative box office earnings (in this context, \"box office\" refers to total cumulative gross revenue).\nFor each film, provide the release year, title, genre, director, lead actor\/actress, total box office revenue (in billions of KRW, rounded to the nearest integer), and total number of admissions.\n\nPlease output the organized data in Markdown table format with the following column headers:\n  Release Year\n  Title\n  Genre\n  Director\n  Lead Actor\/Actress\n  Total Box Office (billions of KRW)\n  Total Admissions\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is:\n```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"title\"], \"required\": [\"releaseyear\", \"title\", \"genre\", \"director\", \"leadactor\/actress\", \"totalboxoffice(billionsofkrw)\", \"totaladmissions\"], \"eval_pipeline\": {\"releaseyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"title\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"totalboxoffice(billionsofkrw)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totaladmissions\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"genre\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"director\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"leadactor\/actress\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_095","query":"Please help me count the Korean dramas that aired on Netflix between January 2024 and December 2024. I need the Drama Title, Premiere Date(YYYY-MM-DD), Director, Number of Episodes and Awards from Baeksang Art Awards and Blue Dragon Film Awards won by the drama, its director. If the drama or related cast and staff have not received any awards, use “\/” to indicate this. By the way, I only need the awarded prizes, not the ones that were only nominated.\n\nPlease output the organized data in Markdown table format.\n The column headers should be as follows:\nDrama Title, Premiere Date, Director, Number of Episodes, Awards.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"dramatitle\"], \"required\": [\"dramatitle\", \"releasedate\", \"director\", \"numberofepisodes\", \"awards\"], \"eval_pipeline\": {\"releasedate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"numberofepisodes\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"dramatitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"director\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"awards\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_095","query":"Please help me count the Korean dramas that aired on Netflix between January 2024 and December 2024. I need the Drama Title, Premiere Date(YYYY-MM-DD), Director, Number of Episodes and Awards from Baeksang Art Awards and Blue Dragon Film Awards won by the drama, its director. If the drama or related cast and staff have not received any awards, use “\/” to indicate this. By the way, I only need the awarded prizes, not the ones that were only nominated.\n\nPlease output the organized data in Markdown table format.\n The column headers should be as follows:\nDrama Title, Premiere Date, Director, Number of Episodes, Awards.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"dramatitle\"], \"required\": [\"dramatitle\", \"premieredate\", \"director\", \"numberofepisodes\", \"awards\"], \"eval_pipeline\": {\"premieredate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"numberofepisodes\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"dramatitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"director\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"awards\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_096","query":"I want to buy a camera to take landscape photos. I heard that Nikon is good at taking landscapes and want to buy a mirrorless camera. Please help me collect the information of all Nikon Z series products as of the first half of 2025. The information I want to know is: effective pixels, weight, maximum frames per second, shutter speed range, highest number of focus points, digital image processor, ISO range, in-camera shock reduction technology.\nThe weight specifically refers to the weight of the camera body itself, including the battery and memory card; The highest number of focus points refers to the higher value between single-point AF and auto-area AF.\n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are: Camera Model, Effective Pixels, Weight, Maximum Frames per Second, Shutter Speed Range, Highest Number of Focus Points, Digital Image Processor, ISO Range, In-camera Vibration Reduction Technology.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"cameramodel\"], \"required\": [\"cameramodel\", \"effectivepixels\", \"weight\", \"maximumframespersecond\", \"shutterspeedrange\", \"highestnumberoffocuspoints\", \"digitalimageprocessor\", \"isorange\", \"in-cameravibrationreductiontechnology\"], \"eval_pipeline\": {\"cameramodel\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"maximumframespersecond\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"weight\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"shutterspeedrange\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"digitalimageprocessor\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"isorange\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"in-cameravibrationreductiontechnology\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"effectivepixels\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"highestnumberoffocuspoints\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_097","query":"Tony Leung is a well-known film actor. Please help me sort out all the movies in which he has acted in and that have been released since his debut. The time span is 2000-2023 (including 2000 and 2023).\nThe sorted information should include: film title, director, leading actress, year of premiere, distributor, and awards (only include the awards he won rather than any awards he was nominated for). By the way, please exclude anthology films, re-edited and re-released, and movies that he's dubbed.\n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are as follows: Movie Title, Director, Leading Actress, Year of Premiere, Distributor, Awards.\nIf some information cannot be retrieved, please output \"-\".\nAwards only refer to the awards won by Tony Leung in this film.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"movietitle\"], \"required\": [\"movietitle\", \"director\", \"leadingactress\", \"yearofpremiere\", \"distributor\", \"awards\"], \"eval_pipeline\": {\"yearofpremiere\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"movietitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"awards\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"director\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"leadingactress\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"distributor\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_098","query":"I'm wondering about the recent development of Africa. Can you list the GDP annual growth rate of all the countries in Sub-Saharan Africa from 2022-2024 (inclusive),  citing trustworthy number from World Bank? If some information cannot be retrieved, please output \"NA\".\n\nPlease output the sorted data in the format of a single Markdown table. The column names in the table are as follows: \nCountry, 2024 GDP growth (%), 2023 GDP growth (%),  2022 GDP growth (%)\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"country\"], \"required\": [\"country\", \"2024gdpgrowth(%)\", \"2023gdpgrowth(%)\", \"2022gdpgrowth(%)\"], \"eval_pipeline\": {\"2024gdpgrowth(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023gdpgrowth(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022gdpgrowth(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}