context stringlengths 362 4.81k | question stringlengths 2.11k 2.25k | answer stringlengths 1 260 | max_new_tokens int64 128 128 | answer_prefix stringclasses 1
value |
|---|---|---|---|---|
Table InputTable: [["Preceded by\\nAlfred Scott", "Member of Parliament for Ashton-under-Lyne\\n1910–1916", "Succeeded by\\nAlbert Stanley"], ["Preceded by\\nSir Frederick Cawley", "Chancellor of the Duchy of Lancaster\\n1918", "Succeeded by\\nThe Lord Downham"], ["New creation", "Baron Beaverbrook\\n1917–1964", "Succeeded by\\nJohn William Maxwell Aitken"], ["New creation", "Baronet(of Cherkley) \\n1916–1964", "Succeeded by\\nJohn William Maxwell Aitken"], ["Preceded by\\nViscount Cranborne", "Lord Privy Seal\\n1943–1945", "Succeeded by\\nArthur Greenwood"], ["Preceded by\\nSir Andrew Duncan", "Minister of Supply\\n1941–1942", "Succeeded by\\nSir Andrew Duncan"], ["New office", "Minister of Information\\n1918", "Succeeded by\\nThe Lord Downham"], ["New office", "Minister of War Production\\n1942", "Succeeded by\\nOliver Lyttelton\\nas Minister of Production"], ["New office", "Minister of Aircraft Production\\n1940–1941", "Succeeded by\\nJohn Moore-Brabazon"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:in what year was lord beaverbrook chancellor of the duchy of lancaster? | 1918 | 128 | Answer: |
Table InputTable: [["Year", "Film", "Role", "Notes"], ["1972", "Eagle in a Cage", "Betty Balcombe", ""], ["2011", "Cockneys vs Zombies", "Doreen", ""], ["1978", "Sweeney 2", "Switchboard Girl", ""], ["1971", "The Boy Friend", "Fay", ""], ["1980", "The Watcher in the Woods", "Young Mrs Aylwood", ""], ["2013", "Still Waters", "Grandma", "In Production"], ["1994", "Beyond Bedlam", "Sister Romulus", ""], ["1976", "Voyage of the Damned", "Lotte Schulman", ""], ["1973", "The Love Ban", "Joyce", ""], ["1986", "Castaway", "Sister Saint Margaret", ""], ["1971", "The Devils", "Phillippe", ""], ["1995", "Jackson: My Life... Your Fault", "Josephine", ""], ["2005", "Mrs Palfrey at the Claremont", "Shirley Burton", ""], ["1997", "Preaching to the Perverted", "Miss Wilderspin", ""], ["1980", "McVicar", "Kate", ""], ["1974", "Butley", "Carol Heasman", ""], ["1979", "The World Is Full of Married Men", "Lori Grossman", ""], ["1975", "Lisztomania", "", "Uncredited Appearance"], ["1977", "Valentino", "", "Uncredited Appearance"], ["2002", "AKA", "Elizabeth of Lithuania", ""], ["1974", "Mahler", "Alma Mahler", "Received BAFTA Award for Most Promising Newcomer"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:when did the movie "eagle in a cage," starring georgina hale, get released in the usa? | 1972 | 128 | Answer: |
Table InputTable: [["#", "Date", "Venue", "Opponent", "Score", "Result", "Competition"], ["8", "March 14, 1993", "Tokyo, Japan", "Japan", "1–0", "1–3", "Friendly"], ["12", "March 26, 1994", "Dallas, Texas", "Bolivia", "1–1", "2–2", "Friendly"], ["11", "February 20, 1994", "Miami, Florida", "Sweden", "1–3", "1–0", "Friendly"], ["6", "April 4, 1992", "Palo Alto, California", "China PR", "1–0", "1-0", "Friendly"], ["5", "March 18, 1992", "Casablanca, Morocco", "Morocco", "1–2", "1–3", "Friendly"], ["7", "April 4, 1992", "Palo Alto, California", "China PR", "5–0", "1-0", "Friendly"], ["10", "December 5, 1993", "Los Angeles, California", "El Salvador", "5–0", "7–0", "Friendly"], ["1", "April 4, 1985", "Portland, Oregon", "Canada", "N/A", "1–1", "Friendly"], ["2", "August 13, 1988", "St. Louis, Missouri", "Jamaica", "2–1", "5-1", "1990 World Cup qualifying"], ["4", "July 3, 1991", "Los Angeles, California", "Costa Rica", "2–2", "3–2", "1991 CONCACAF Gold Cup"], ["3", "September 17, 1989", "Tegucigalpa, Honduras", "El Salvador", "1–0", "1–0", "1990 World Cup qualifying"], ["9", "October 16, 1993", "High Point, North Carolina", "Ukraine", "1–0", "1–2", "Friendly"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what venue was immediately after tokyo, japan? | High Point, North Carolina | 128 | Answer: |
Table InputTable: [["Olympics", "Athlete", "Judge (Official)", "Coach", "Language"], ["1976 Summer Olympics", "Pierre St.-Jean", "Maurice Fauget", "-", "French (St.-Jean)/English (Fauget)"], ["1984 Summer Olympics", "Edwin Moses", "Sharon Weber", "-", "English"], ["1956 Summer Olympics", "John Landy (Melbourne)\\nHenri Saint Cyr (Stockholm)", "-", "-", "English/Swedish"], ["1936 Summer Olympics", "Rudolf Ismayr", "-", "-", "-"], ["1972 Summer Olympics", "Heidi Schüller", "Heinz Pollay", "-", "German"], ["1948 Summer Olympics", "Donald Finlay", "-", "-", "English"], ["2012 Summer Olympics", "Sarah Stevenson", "Mik Basi", "Eric Farrell", "English"], ["1996 Summer Olympics", "Teresa Edwards", "Hobie Billingsley", "-", "English"], ["2000 Summer Olympics", "Rechelle Hawkes", "Peter Kerr", "-", "English"], ["1992 Summer Olympics", "Luis Doreste Blanco", "Eugeni Asensio", "-", "Spanish/Catalan"], ["1936 Winter Olympics", "Willy Bogner, Sr.", "-", "-", "-"], ["1928 Summer Olympics", "Harry Dénis", "-", "-", "-"], ["1980 Winter Olympics", "Eric Heiden", "Terry McDermott", "-", "English"], ["1988 Winter Olympics", "Pierre Harvey", "Suzanna Morrow-Francis", "-", "English"], ["1924 Summer Olympics", "Géo André", "-", "-", "French."], ["1992 Winter Olympics", "Surya Bonaly", "Pierre Bornat", "-", "French"], ["1994 Winter Olympics", "Vegard Ulvang", "Kari Kåring", "-", "English (Ulvang)/Norwegian (Kåring)"], ["1960 Summer Olympics", "Adolfo Consolini", "-", "-", "-"], ["1932 Summer Olympics", "George Calnan", "-", "-", "English"], ["2010 Winter Olympics", "Hayley Wickenheiser", "Michel Verrault", "-", "English/French"], ["2008 Summer Olympics", "Zhang Yining", "Huang Liping", "-", "Chinese"], ["1988 Summer Olympics", "Hur Jae\\nShon Mi-Na", "Lee Hak-Rae", "-", "Korean"], ["1980 Summer Olympics", "Nikolai Andrianov", "Alexander Medved", "-", "Russian"], ["1968 Summer Olympics", "Pablo Garrido", "-", "-", "Spanish"], ["1960 Winter Olympics", "Carol Heiss", "-", "-", "-"], ["1964 Winter Olympics", "Paul Aste", "-", "-", "German"], ["1920 Summer Olympics", "Victor Boin", "-", "-", "-"], ["1968 Winter Olympics", "Léo Lacroix", "-", "-", "French"], ["1972 Winter Olympics", "Keiichi Suzuki", "Fumio Asaki", "-", "Japanese"], ["1964 Summer Olympics", "Takashi Ono", "-", "-", "Japanese"], ["2006 Winter Olympics", "Giorgio Rocca", "Fabio Bianchetti", "-", "Italian"], ["1928 Winter Olympics", "Hans Eidenbenz", "-", "-", "-"], ["1924 Winter Olympics", "Camille Mandrillon", "-", "-", "-"], ["1948 Winter Olympics", "Bibi Torriani", "-", "-", "-"], ["1952 Summer Olympics", "Heikki Savolainen", "-", "-", "-"], ["2002 Winter Olympics", "Jimmy Shea", "Allen Church", "-", "English"], ["1976 Winter Olympics", "Werner Delle Karth", "Willy Köstinger", "-", "German"], ["1998 Winter Olympics", "Kenji Ogiwara", "Junko Hiramatsu", "-", "Japanese"], ["2004 Summer Olympics", "Zoi Dimoschaki", "Lazaros Voreadis", "-", "Greek"], ["1932 Winter Olympics", "Jack Shea", "-", "-", "-"], ["1956 Winter Olympics", "Giuliana Minuzzo", "-", "-", "-"], ["1952 Winter Olympics", "Torbjørn Falkanger", "-", "-", "-"], ["1984 Winter Olympics", "Bojan Križaj", "Dragan Perovic", "-", "Serbo-Croatian"], ["2014 Winter Olympics", "Ruslan Zakharov", "Vyacheslav Vedenin, Jr", "Anastassia Popkova", "Russian"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the name of the last speaker? | Ruslan Zakharov | 128 | Answer: |
Table InputTable: [["Series Number", "Season Number", "Episode Title", "Premiere Date", "Production Code"], ["5", "5", "Hurricane Jules", "November 22, 1998", "105"], ["1", "1", "Going Up!", "October 25, 1998", "101"], ["4", "4", "Close Encounters", "November 15, 1998", "104"], ["12", "12", "Bottom's Up", "March 7, 1999", "112"], ["2", "2", "Who's The Man", "November 1, 1998", "102"], ["3", "3", "Vootle-Muck-A-Heev", "November 8, 1998", "103"], ["13", "13", "Hot Dog", "March 14, 1999", "113"], ["7", "7", "Front Page", "January 17, 1999", "107"], ["11", "11", "JB's Big Break", "February 21, 1999", "111"], ["6", "6", "Switcheroo", "November 29, 1998", "106"], ["8", "8", "Special FX-Ation", "January 24, 1999", "108"], ["9", "9", "The Famous Stone Gold", "January 31, 1999", "109"], ["10", "10", "Kiss And Tell", "February 7, 1999", "110"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the name of the next title after hurricane jules? | Switcheroo | 128 | Answer: |
Table InputTable: [["Year", "Division", "League", "Regular Season", "Playoffs", "Open Cup"], ["2009", "4", "USL PDL", "6th, Heartland", "Did not qualify", "Did not qualify"], ["2004", "4", "USL PDL", "6th, Heartland", "Did not qualify", "Did not qualify"], ["2008", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2012", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2006", "4", "USL PDL", "3rd, Heartland", "Did not qualify", "Did not qualify"], ["2007", "4", "USL PDL", "3rd, Heartland", "Did not qualify", "1st Round"], ["2010", "4", "USL PDL", "7th, Heartland", "Did not qualify", "Did not qualify"], ["1999", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2013", "4", "USL PDL", "4th, Heartland", "Did not qualify", "Did not qualify"], ["2003", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2005", "4", "USL PDL", "3rd, Heartland", "Did not qualify", "Did not qualify"], ["2002", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2011", "4", "USL PDL", "4th, Heartland", "Did not qualify", "Did not qualify"], ["2001", "4", "USL PDL", "5th, Rocky Mountain", "Did not qualify", "Did not qualify"], ["1998", "4", "USISL PDSL", "4th, Central", "Division Finals", "1st Round"], ["2000", "4", "USL PDL", "4th, Rocky Mountain", "Did not qualify", "Did not qualify"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:when did they get 6th place in the regular season before their 6th place finish in 2009? | 2004 | 128 | Answer: |
Table InputTable: [["Wrestler:", "Reigns:", "Date:", "Place:", "Notes:"], ["Randy Savage", "3", "1982", "Unknown", ""], ["Randy Savage", "2", "1981", "Unknown", ""], ["Randy Savage", "1", "March 13, 1979", "Halifax, Nova Scotia", ""], ["Paul Christy", "1", "November 13, 1983", "Springfield, Illinois", ""], ["Lanny Poffo", "4", "January 1, 1984", "Springfield, Illinois", ""], ["Lanny Poffo", "1", "May 10, 1978", "San Francisco, California", "Defeated Joe Banek to become the first champion"], ["Lanny Poffo", "3", "1981", "Unknown", ""], ["Lanny Poffo", "2", "July 21, 1979", "Lexington, Kentucky", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the total number of reigns for randy savage? | 6 | 128 | Answer: |
Table InputTable: [["No.", "Score", "Player", "Team", "Balls", "Inns.", "Opposing team", "Date", "Result"], ["9", "107", "Virat Kohli", "India", "114", "2", "Sri Lanka", "24 December 2009", "Won"], ["6", "134*", "Graeme Smith", "South Africa", "124", "2", "India", "25 November 2005", "Won"], ["11", "101", "Paul Stirling", "Ireland", "72", "2", "Netherlands", "18 March 2011", "Won"], ["12", "106", "Nasir Jamshed", "Pakistan", "124", "1", "India", "3 January 2013", "Won"], ["2", "107*", "Desmond Haynes", "West Indies", "137", "1", "Pakistan", "1 November 1989", "Lost"], ["3", "100*", "Sachin Tendulkar", "India", "103", "2", "Kenya", "31 May 1998", "Won"], ["5", "108*", "Salman Butt", "Pakistan", "130", "2", "India", "13 November 2004", "Won"], ["8", "150*", "Gautam Gambhir", "India", "137", "2", "Sri Lanka", "24 December 2009", "Won"], ["10", "106", "Ryan ten Doeschate", "Netherlands", "108", "1", "Ireland", "18 March 2011", "Lost"], ["4", "121", "Marcus Trescothick", "England", "109", "2", "India", "19 January 2002", "Lost"], ["7", "118", "Upul Tharanga", "Sri Lanka", "128", "1", "India", "24 December 2009", "Lost"], ["1", "123", "Kris Srikkanth", "India", "103", "1", "Pakistan", "18 February 1987", "Lost"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many teams are there that are not also opposing teams? | 3 | 128 | Answer: |
Table InputTable: [["Rank", "Name", "Club", "Nationality", "Points"], ["8", "Alessandro Mazzola", "Internazionale", "Italy", "9"], ["15", "Mario Corso", "Internazionale", "Italy", "3"], ["7", "Gianni Rivera", "Milan", "Italy", "10"], ["2", "Giacinto Facchetti", "Internazionale", "Italy", "59"], ["17", "Milan Galić", "Partizan Beograd", "Yugoslavia", "2"], ["3", "Luis Suárez", "Internazionale", "Spain", "45"], ["17", "Amancio Amaro", "Real Madrid", "Spain", "2"], ["1", "Eusébio", "Benfica", "Portugal", "67"], ["17", "Mário Coluna", "Benfica", "Portugal", "2"], ["13", "Ferenc Puskás", "Real Madrid", "Spain", "5"], ["25", "Sigfried Held", "Borussia Dortmund", "West Germany", "1"], ["12", "Karl-Heinz Schnellinger", "Milan", "West Germany", "6"], ["5", "Bobby Charlton", "Manchester United", "England", "19"], ["6", "Flórián Albert", "Ferencvárosi", "Hungary", "14"], ["17", "Slava Metreveli", "Dinamo Tbilisi", "Soviet Union", "2"], ["11", "Denis Law", "Manchester United", "Scotland", "8"], ["17", "Franz Beckenbauer", "Bayern Munich", "West Germany", "2"], ["17", "Philippe Gondet", "Nantes", "France", "2"], ["8", "Georgi Asparuhov", "Levski Sofia", "Bulgaria", "9"], ["25", "Ivor Allchurch", "Cardiff City", "Wales", "1"], ["17", "Ferenc Bene", "Ujpest Dozsa", "Hungary", "2"], ["4", "Paul Van Himst", "Anderlecht", "Belgium", "25"], ["15", "Lev Yashin", "Dynamo Moscow", "Soviet Union", "3"], ["13", "Jim Baxter", "Sunderland", "Scotland", "5"], ["17", "Andrej Kvašňák", "Sparta Praha", "Czechoslovakia", "2"], ["25", "Jakob Kühn", "Zürich", "Switzerland", "1"], ["8", "Valery Voronin", "Torpedo Moskva", "Soviet Union", "9"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the average number of points scored by italians? | 20.25 | 128 | Answer: |
Table InputTable: [["Hull", "Type", "Original Name", "Original Operator", "Delivery", "Disposition (2012)", "2nd name", "2nd operator", "3rd Name", "3rd operator"], ["№ 9", "929-117", "Venus", "Kyushu Yusen", "Mar 1991", "Active", "", "", "", ""], ["№ 12", "929-117", "Toppy 2", "Tane Yaku Jetfoils", "Apr 1992", "Active", "", "", "", ""], ["№ 11", "929-117", "Princess Teguise", "Trasmediterranea", "Jun 1991", "Active", "2007 Toppy 5", "Tane Yaku Jetfoils", "", ""], ["№ 10", "929-117", "Suisei", "Sado Kisen", "Apr 1991", "Active", "", "", "", ""], ["№ 13", "929-117", "Toppy 3", "Tane Yaku Jetfoils", "Mar 1995", "Active", "", "", "", ""], ["№ 4", "929-117", "Princess Dacil", "Trasmediterranea", "Mar 1990", "Active", "Pegasus", "Kyusyu Shosen Co. Ltd.", "", ""], ["№ 7", "929-117", "Unicorn", "Kyusyu Shosen Co. Ltd.", "Oct 1990", "Active", "Pegasus 2", "Kyusyu Shosen Co. Ltd.", "", ""], ["№ 2", "929-117", "Pegasus", "Kyusyu Shosen Co. Ltd.", "Jun 1989", "Active", "Toppy 1", "Tane Yaku Jetfoils", "", ""], ["№ 8", "929-117", "Beetle 2", "JR Kyushu Jet Ferries", "Feb 1991", "Active", "", "", "", ""], ["№ 3", "929-117", "Toppy 1", "Tane Yaku Jetfoils", "Sep 1989", "Active", "Beetle 3", "JR Kyushu Jet Ferries", "", ""], ["№ 15", "929-117", "Emerald Wing", "Kaijo Access Co.", "Jun 1994", "Active", "2004 Rocket 1", "Cosmo Line", "-", "Tane Yaku Jetfoil"], ["№ 5", "929-117", "Nagasaki", "JR Kyushu Jet Ferries", "Apr 1990", "Active", "Beetle 1", "JR Kyushu Jet Ferries", "", ""], ["№ 1", "929-117", "Tsubasa", "Sado Kisen", "Mar 1998", "Active", "", "", "", ""], ["№ 6", "929-117", "Beetle", "JR Kyushu Jet Ferries", "Jul 1990", "Active", "Rocket", "Cosmo Line", "Rocket 3", "Tane Yaku Jetfoils"], ["№ 14", "929-117", "Crystal Wing", "Kaijo Access Co.", "Jun 1994", "Active", "2002 Beetle 5", "JR Kyushu Jet Ferries", "", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what original name is after venus in apr 1991? | Suisei | 128 | Answer: |
Table InputTable: [["#", "Date", "Location", "Winner", "Score\\nJSU", "Score\\nTU", "Series"], ["2", "October 28, 1927", "?", "Jacksonville State", "26", "12", "JSU 2–0"], ["8", "November 11, 1938", "Jacksonville, AL", "Tied", "6", "6", "TSU 4–3–1"], ["1", "November 27, 1924", "Jacksonville, AL", "Jacksonville State", "14", "9", "JSU 1–0"], ["14", "December 18, 1948", "Pensacola, FL", "Jacksonville State", "19", "0", "TSU 7–6–1"], ["9", "November 11, 1939", "Troy, AL", "Troy State", "0", "27", "TSU 5–3–1"], ["3", "November 16, 1928", "Troy, AL", "Jacksonville State", "20", "0", "JSU 3–0"], ["38", "November 11, 1972", "Jacksonville, AL", "Tied", "14", "14", "JSU 22–14–2"], ["7", "October 26, 1934", "Troy, AL", "Troy State", "0", "32", "TSU 4–3"], ["10", "November 8, 1940", "Troy, AL", "Troy State", "0", "7", "TSU 6–3–1"], ["20", "October 16, 1954", "Jacksonville, AL", "Jacksonville State", "38", "7", "TSU 10–9–1"], ["6", "November 10, 1933", "Jacksonville, AL", "Troy State", "7", "18", "Tied 3–3"], ["48", "November 13, 1982", "Jacksonville, AL", "Jacksonville State", "49", "14", "JSU 29–17–2"], ["56", "November 3, 1990", "Jacksonville, AL", "Jacksonville State", "21", "10", "JSU 32–22–2"], ["4", "October 3, 1931", "Jacksonville, AL", "Troy State", "6", "24", "JSU 3–1"], ["15", "October 15, 1949", "Troy, AL", "Troy State", "6", "27", "TSU 8–6–1"], ["", "Totals", "", "", "1086", "1110", "JSU 32–29–2"], ["21", "October 15, 1955", "Troy, AL", "Jacksonville State", "12", "0", "Tied 10–10–1"], ["40", "November 16, 1974", "Jacksonville, AL", "Jacksonville State", "23", "12", "JSU 24–14–2"], ["36", "October 17, 1970", "Jacksonville, AL", "Jacksonville State", "55", "10", "JSU 22–13–1"], ["49", "November 12, 1983", "Troy, AL", "Troy State", "3", "45", "JSU 29–18–2"], ["44", "November 11, 1978", "Jacksonville, AL", "Jacksonville State", "42", "21", "JSU 26–16–2"], ["39", "November 10, 1973", "Troy, AL", "Jacksonville State", "38", "14", "JSU 23–14–2"], ["5", "November 12, 1932", "Montgomery, AL", "Troy State", "0", "20", "JSU 3–2"], ["45", "November 10, 1979", "Troy, AL", "Troy State", "10", "12", "JSU 26–17–2"], ["13", "October 14, 1948", "Jacksonville, AL", "Jacksonville State", "25", "13", "TSU 7–5–1"], ["46", "November 15, 1980", "Jacksonville, AL", "Jacksonville State", "13", "8", "JSU 27–17–2"], ["54", "November 5, 1988", "Jacksonville, AL", "Jacksonville State", "31", "3", "JSU 30–22–2"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:in years, how long has it been since the first game listed? | 70 | 128 | Answer: |
Table InputTable: [["Chord", "Root", "Minor Third", "Perfect Fifth", "Major Seventh"], ["DmM7", "D", "F", "A", "C♯"], ["GmM7", "G", "B♭", "D", "F♯"], ["EmM7", "E", "G", "B", "D♯"], ["D♭mM7", "D♭", "F♭ (E)", "A♭", "C"], ["CmM7", "C", "E♭", "G", "B"], ["AmM7", "A", "C", "E", "G♯"], ["BmM7", "B", "D", "F♯", "A♯"], ["FmM7", "F", "A♭", "C", "E"], ["B♭mM7", "B♭", "D♭", "F", "A"], ["E♭mM7", "E♭", "G♭", "B♭", "D"], ["D♯mM7", "D♯", "F♯", "A♯", "C (D)"], ["G♭mM7", "G♭", "B (A)", "D♭", "F"], ["G♯mM7", "G♯", "B", "D♯", "F (G)"], ["A♭mM7", "A♭", "C♭ (B)", "E♭", "G"], ["F♯mM7", "F♯", "A", "C♯", "E♯ (F)"], ["A♯mM7", "A♯", "C♯", "E♯ (F)", "G (A)"], ["C♯mM7", "C♯", "E", "G♯", "B♯ (C)"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:is dm above or below am? | above | 128 | Answer: |
Table InputTable: [["Date", "Venue", "Opponent", "Result", "Scoreline", "China scorers"], ["Dec 17", "Muscat", "Oman", "Lost", "1-3", "Qu Bo 58'"], ["Dec 19", "Muscat", "Iran", "Lost", "0-2", ""], ["April 23", "Los Angeles", "El Salvador", "Drawn", "2-2", "Xiao Zhanbo 62' pen\\nQu Bo 63'"], ["January 20", "Zhongshan", "Lebanon", "Drawn", "0-0", "—"], ["March 15", "Kunming", "Thailand", "Drawn", "3-3", "Qu Bo 34'\\nHan Peng 67'\\nZhu Ting 90'"], ["April 16", "Seattle", "Mexico", "Lost", "0-1", "—"], ["Dec 21", "Amman", "Jordan", "Won", "1-0", "Cao Yang 77'"], ["January 27", "Zhongshan", "Syria", "Won", "2-1", "Qu Bo 64'\\nZhu Ting 90'"], ["January 10", "Dubai", "United Arab Emirates", "Drawn", "0-0", "—"], ["May 25", "Kunshan", "Jordan", "Won", "2-0", "Hao Junmin 23' pen\\nLi Weifeng 48'"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the last date? | Dec 21 | 128 | Answer: |
Table InputTable: [["Country", "Amphibians", "Birds", "Mammals", "Reptile", "Total terrestrial vertebrates", "Vascular plants", "Biodiversity"], ["Guatemala", "133", "684", "193", "236", "1246", "8681", "9927"], ["Belize", "46", "544", "147", "140", "877", "2894", "3771"], ["Costa Rica", "183", "838", "232", "258", "1511", "12119", "13630"], ["Honduras", "101", "699", "201", "213", "1214", "5680", "6894"], ["El Salvador", "30", "434", "137", "106", "707", "2911", "3618"], ["Nicaragua", "61", "632", "181", "178", "1052", "7590", "8642"], ["Panama", "182", "904", "241", "242", "1569", "9915", "11484"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many species of birds are there in guatemala? | 684 | 128 | Answer: |
Table InputTable: [["Model", "1991", "1995", "1996", "1997", "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013"], ["Škoda Superb", "−", "−", "−", "−", "−", "−", "−", "177", "16,867", "23,135", "22,392", "22,091", "20,989", "20,530", "25,645", "44,548", "98,873", "116,700", "106,847", "94,400"], ["Škoda Rapid", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "1,700", "9,292", "103,800"], ["Škoda Felicia", "172,000", "210,000", "", "288,458", "261,127", "241,256", "148,028", "44,963", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−"], ["Škoda Octavia", "−", "−", "", "47,876", "102,373", "143,251", "158,503", "164,134", "164,017", "165,635", "181,683", "233,322", "270,274", "309,951", "344,857", "317,335", "349,746", "387,200", "409,360", "359,600"], ["Škoda Fabia", "−", "−", "−", "−", "−", "823", "128,872", "250,978", "264,641", "260,988", "247,600", "236,698", "243,982", "232,890", "246,561", "264,173", "229,045", "266,800", "255,025", "202,000"], ["Škoda Roomster", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "14,422", "66,661", "57,467", "47,152", "32,332", "36,000", "39,249", "33,300"], ["Škoda Yeti", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "11,018", "52,604", "70,300", "90,952", "82,400"], ["Škoda Citigo", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "509", "36,687", "45,200"], ["Total", "172,000", "210,000", "261,000", "336,334", "363,500", "385,330", "435,403", "460,252", "445,525", "449,758", "451,675", "492,111", "549,667", "630,032", "674,530", "684,226", "762,600", "879,200", "949,412", "920,800"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what year did skoda begin selling more than 300,000 cars? | 1997 | 128 | Answer: |
Table InputTable: [["Week", "Date", "Opponent", "Score", "Result", "Record"], ["5", "Sept 25", "vs. Hamilton Tiger-Cats", "38–12", "Loss", "1–5"], ["10", "Oct 30", "vs. Hamilton Tiger-Cats", "30–9", "Loss", "1–11"], ["3", "Sept 11", "at Toronto Argonauts", "12–5", "Win", "1–3"], ["12", "Nov 13", "vs. Montreal Alouettes", "14–12", "Win", "2–12"], ["6", "Oct 2", "at Hamilton Tiger-Cats", "45–0", "Loss", "1–6"], ["9", "Oct 23", "at Hamilton Tiger-Cats", "25–17", "Loss", "1–10"], ["4", "Sept 18", "vs. Toronto Argonauts", "34–6", "Loss", "1–4"], ["8", "Oct 16", "vs. Toronto Argonauts", "27–11", "Loss", "1–9"], ["7", "Oct 9", "vs. Montreal Alouettes", "25–11", "Loss", "1–7"], ["7", "Oct 11", "at Montreal Alouettes", "24–6", "Loss", "1–8"], ["2", "Sept 6", "vs. Montreal Alouettes", "20–11", "Loss", "0–3"], ["11", "Nov 6", "at Toronto Argonauts", "18–12", "Loss", "1–12"], ["2", "Sept 4", "at Montreal Alouettes", "21–2", "Loss", "0–2"], ["1", "Aug 28", "at Toronto Argonauts", "13–6", "Loss", "0–1"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the total number of wins? | 2 | 128 | Answer: |
Table InputTable: [["Sl no", "Name of the prabandham", "Starting from", "Ending with", "Number of pasurams", "Sung by"], ["", "Total number of pasurams", "", "", "3776", ""], ["18", "Thiruvasiriyam", "2578", "2584", "7", "Nammalvar"], ["16", "Naanmugan Thiruvandhadhi", "2382", "2477", "96", "Thirumalisai alvar"], ["11", "Kurun Thandagam", "2032", "2051", "20", "Thirumangai alvar"], ["12", "Nedum Thandagam", "2052", "2081", "30", "Thirumangai alvar"], ["4", "Perumal Thirumozhi", "647", "751", "105", "Kulasekara alvar"], ["8", "Amalanadhi piran", "927", "936", "10", "Thiruppaan alvar"], ["17", "Thiruviruththam", "2478", "2577", "100", "Nammalvar"], ["20", "Thiruvezhukkurrirukkai", "2672", "2672", "1", "Thirumangai alvar"], ["21", "Siriya Thirumadal", "2673", "2673", "1", "Thirumangai alvar"], ["3", "Nachiar Tirumozhi", "504", "646", "143", "Aandaal"], ["22", "Peria Thirumadal", "2674", "2674", "1", "Thirumangai alvar"], ["9", "Kanni Nun Siruththambu", "937", "947", "11", "Madhurakavi Alvar"], ["2", "Thiruppavai", "474", "503", "30", "Aandaal"], ["15", "Moonram Thiruvandhadhi", "2282", "2381", "100", "Peyalvar"], ["10", "Peria Thirumozhi", "948", "2031", "1084", "Thirumangai alvar"], ["13", "Mudhal Thiruvandhadhi", "2082", "2181", "100", "Poigai Alvar"], ["6", "Thirumalai", "872", "916", "45", "Thondaradippodi alvar"], ["19", "Peria Thiruvandhadhi", "2585", "2671", "87", "Nammalvar"], ["5", "Thiruchchanda Viruththam", "752", "871", "120", "Thirumalisai alvar"], ["14", "Irandam Thiruvandhadhi", "2182", "2281", "100", "Bhoothathalvar"], ["23", "Thiruvay Mozhi", "2674", "3776", "1102", "Nammalvar"], ["1", "Periazhvar Thirumozhi", "1", "473", "473", "Periyalvar"], ["7", "Thiruppalliyezhuchchi", "917", "926", "10", "Thondaradippodi alvar"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many pasurams are in naanmugan | 96 | 128 | Answer: |
Table InputTable: [["Number", "Name", "Term Started", "Term Ended", "Alma Mater", "Field(s)", "Educational Background"], ["1", "Dr Abdus Salam", "1961", "1967", "Imperial College", "Theoretical Physics", "Doctor of Philosophy (Ph.D)"], ["3", "Air Commodore K. M. Ahmad", "1979", "1980", "Pakistan Air Force Academy", "Flight Instructor", "Certificated Flight Instructor (CFI)"], ["7", "Dr Abdul Majid", "1997", "2001", "University of Wales", "Astrophysics", "Ph.D"], ["8", "Major General Raza Hussain", "2001", "2010", "Pakistan Army Corps of Electrical and Mechanical Engineers", "Electrical Engineering", "B.S."], ["5", "Dr M. Shafi Ahmad", "1989", "1990", "University of London", "Astronomy", "Ph.D"], ["9", "Major General Ahmed Bilal", "2010", "Present", "Pakistan Army Corps of Signals Engineering", "Computer Engineering", "Master of Science (M.S)"], ["2", "Air Commodore Dr Władysław Turowicz", "1967", "1979", "Warsaw University of Technology", "Aeronautical Engineering", "Ph.D"], ["6", "Engr.Sikandar Zaman", "1990", "1997", "University of Leeds", "Mechanical Engineering", "Bachelor of Science (B.S.)"], ["4", "Dr Salim Mehmud", "1980", "1989", "Oak Ridge Institute for Science and Education and Oak Ridge National Laboratory", "Nuclear Engineering, Electrical engineering, Physics, Mathematics, Electronics engineering", "Ph.D"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who was the last administrator of suparco? | Major General Ahmed Bilal | 128 | Answer: |
Table InputTable: [["District", "Location", "Communities served"], ["Solon/Bainbridge Montessori School of Languages", "Bainbridge Township, Ohio", "nonsectarian Montessori School: quarterly enrollment periods"], ["Hershey Montessori Farm School", "Huntsburg Township, Ohio", "parent-owned, and chartered by Ohio Department of Education: application deadline January each year"], ["Saint Anselm School", "Chester Township, Ohio", "Roman Catholic Diocese of Cleveland K - 8th grade; preschool"], ["Hawken School", "Gates Mills, Ohio", "College preparatory day school: online application, site visit and testing"], ["Notre Dame-Cathedral Latin", "Munson Township, Ohio", "Roman Catholic Diocese of Cleveland: open to 8th grade students who have attended a Catholic elementary school and others who have not"], ["Saint Mary's School", "Chardon, Ohio", "Roman Catholic Diocese of Cleveland preschool - 8th grade; parishioners and non-parishioners"], ["Agape Christian Academy", "Burton Township, Ohio and Troy Township, Ohio", "Accepts applications prior to the start of each school year"], ["Saint Helen's School", "Newbury, Ohio", "Roman Catholic Diocese of Cleveland K - 8th grade; parishioners and non-parishioners"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many of the schools are montessori schools? | 1 | 128 | Answer: |
Table InputTable: [["Year", "Winner", "Age", "Jockey", "Trainer", "Owner", "Distance\\n(Miles)", "Time", "Purse", "Gr"], ["1966", "Williamston Kid †", "3", "Robert Stevenson", "James Bartlett", "Ternes & Bartlett", "1-1/8", "1:50.60", "", ""], ["1969", "Top Knight", "3", "Manuel Ycaza", "Ray Metcalf", "Steven B. Wilson", "1-1/8", "1:48.40", "", ""], ["1959", "Easy Spur", "3", "Bill Hartack", "Paul L. Kelley", "Spring Hill Farm", "1-1/8", "1:47.20", "", ""], ["1956", "Needles", "3", "David Erb", "Hugh L. Fontaine", "D & H Stable", "1-1/8", "1:48.60", "", ""], ["1981", "Lord Avie", "3", "Chris McCarron", "Daniel Perlsweig", "David Simon", "1-1/8", "1:50.40", "$250,000", "I"], ["1957", "Gen. Duke", "3", "Bill Hartack", "Horace A. Jones", "Calumet Farm", "1-1/8", "1:46.80", "", ""], ["1967", "In Reality", "3", "Earlie Fires", "Melvin Calvert", "Frances A. Genter", "1-1/8", "1:50.20", "", ""], ["1962", "Ridan", "3", "Manuel Ycaza", "LeRoy Jolley", "Jolley / Woods / Greer", "1-1/8", "1:50.40", "", ""], ["1970", "My Dad George", "3", "Ray Broussard", "Frank J. McManus", "Raymond M. Curtis", "1-1/8", "1:50.80", "", ""], ["1960", "Bally Ache", "3", "Bobby Ussery", "Homer Pitt", "Edgehill Farm", "1-1/8", "1:47.60", "", ""], ["1958", "Tim Tam", "3", "Bill Hartack", "Horace A. Jones", "Calumet Farm", "1-1/8", "1:49.20", "", ""], ["2014", "Constitution", "3", "Javier Castellano", "Todd Pletcher", "Winstar Farm", "1-1/8", "1:49.17", "$1,000,000", "I"], ["1971", "Eastern Fleet", "3", "Eddie Maple", "Reggie Cornell", "Calumet Farm", "1-1/8", "1:47.40", "", ""], ["2007", "Scat Daddy", "3", "Edgar Prado", "Todd A. Pletcher", "J. Scatuorchio / M. Tabor", "1-1/8", "1:49.00", "$1,000,000", "I"], ["1968", "Forward Pass", "3", "Don Brumfield", "Henry Forrest", "Calumet Farm", "1-1/8", "1:49.00", "", ""], ["1986", "Snow Chief", "3", "Alex Solis", "Melvin F. Stute", "Rochelle/Grinstead", "1-1/8", "1:51.80", "$500,000", "I"], ["1953", "Money Broker", "3", "Alfred Popara", "Vester R. Wright", "G. & G. Stable", "1-1/8", "1:53.80", "", ""], ["1965", "Native Charger", "3", "John L. Rotz", "Ray Metcalf", "Warner Stable", "1-1/8", "1:51.20", "", ""], ["1954", "Correlation", "3", "Bill Shoemaker", "Noble Threewitt", "Robert S. Lytle", "1-1/8", "1:55.20", "", ""], ["1963", "Candy Spots", "3", "Bill Shoemaker", "Mesh Tenney", "Rex C. Ellsworth", "1-1/8", "1:50.60", "", ""], ["1998", "Cape Town †", "3", "Shane Sellers", "D. Wayne Lukas", "Overbrook Farm", "1-1/8", "1:49.21", "$750,000", "I"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is their main age listed? | 3 | 128 | Answer: |
Table InputTable: [["No.", "Date", "Tournament", "Winning score", "Margin of\\nvictory", "Runner(s)-up"], ["3", "25 Apr 2004", "Tsuruya Open", "−9 (64-73-69-69=275)", "2 strokes", "Keiichiro Fukabori, Scott Laycock,\\n Tatsuya Mitsuhashi, Taichi Teshima,\\n Shinichi Yokota"], ["9", "26 Sep 2010", "Asia-Pacific Panasonic Open\\n(co-sanctioned by the Asian Tour)", "−6 (71-70-66=207)", "1 stroke", "Ryuichi Oda"], ["6", "22 Apr 2007", "Tsuruya Open", "−16 (67-65-68-68=268)", "2 strokes", "Masahiro Kuramoto, Hirofumi Miyase,\\n Takuya Taniguchi"], ["5", "23 Apr 2006", "Tsuruya Open", "−11 (70-68-66-69=273)", "2 strokes", "Mamo Osanai"], ["11", "15 Apr 2012", "Token Homemate Cup", "−15 (68-69-70-62=269)", "2 strokes", "Ryuichi Oda"], ["1", "3 Nov 2002", "Philip Morris K.K. Championship", "−19 (65-67-67-70=269)", "2 strokes", "Toshimitsu Izawa"], ["8", "2 Dec 2007", "Golf Nippon Series JT Cup", "−11 (70-70-68-61=261)", "1 stroke", "Toru Taniguchi"], ["10", "1 May 2011", "The Crowns", "−9 (67-66-68-70=271)", "Playoff", "Jang Ik-jae"], ["7", "11 Nov 2007", "Mitsui Sumitomo VISA Taiheiyo Masters", "−13 (67-68-69-70=274)", "5 strokes", "Toru Taniguchi"], ["13", "30 Jun 2013", "Gateway to the Open Mizuno Open", "−19 (67-66-68-68=269)", "3 strokes", "Kim Kyung-tae"], ["4", "26 Jun 2004", "Gateway to the Open Mizuno Open", "−14 (67-68-70-69=274)", "Playoff", "Hiroaki Iijima"], ["2", "10 Aug 2003", "Sun Chlorella Classic", "−8 (71-73-68-68=280)", "1 stroke", "Daisuke Maruyama, Taichi Teshima"], ["12", "29 Jul 2012", "Sun Chlorella Classic", "−15 (69-66-68-70=273)", "2 strokes", "Lee Seong-ho, Hideki Matsuyama (am),\\n Yoshinobu Tsukada"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which is the only tournament with a british runner-up? | Tsuruya Open | 128 | Answer: |
Table InputTable: [["Year", "Car", "Start", "Qual", "Rank", "Finish", "Laps", "Led", "Retired"], ["1928", "8", "4", "117.031", "4", "10", "200", "33", "Running"], ["1939", "62", "27", "121.749", "24", "11", "200", "0", "Running"], ["1937", "38", "7", "118.788", "16", "8", "200", "0", "Running"], ["1927", "27", "27", "107.765", "22", "3", "200", "0", "Running"], ["1933", "34", "12", "113.578", "15", "7", "200", "0", "Running"], ["1929", "23", "11", "112.146", "15", "17", "91", "0", "Supercharger"], ["1931", "37", "19", "111.725", "6", "18", "167", "0", "Crash T4"], ["1932", "25", "20", "108.896", "34", "13", "184", "0", "Flagged"], ["1938", "17", "4", "122.499", "6", "17", "130", "0", "Rod"], ["1926", "31", "12", "102.789", "13", "11", "142", "0", "Flagged"], ["1930", "9", "20", "100.033", "18", "20", "79", "0", "Valve"], ["1934", "8", "7", "113.733", "13", "17", "94", "0", "Rod"], ["1935", "44", "6", "115.459", "11", "21", "102", "0", "Magneto"], ["Totals", "Totals", "Totals", "Totals", "Totals", "Totals", "1989", "33", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the most laps run before being retired? | 200 | 128 | Answer: |
Table InputTable: [["#", "Player", "Goals", "Caps", "Career"], ["8", "Eddie Johnson", "19", "62", "2004–present"], ["1", "Landon Donovan", "57", "155", "2000–present"], ["9T", "Earnie Stewart", "17", "101", "1990–2004"], ["2", "Clint Dempsey", "36", "103", "2004–present"], ["4", "Brian McBride", "30", "95", "1993–2006"], ["6T", "Bruce Murray", "21", "86", "1985–1993"], ["9T", "DaMarcus Beasley", "17", "114", "2001–present"], ["3", "Eric Wynalda", "34", "106", "1990–2000"], ["5", "Joe-Max Moore", "24", "100", "1992–2002"], ["6T", "Jozy Altidore", "21", "67", "2007–present"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the total number of goals scored by johnson and stewart? | 36 | 128 | Answer: |
Table InputTable: [["Year", "Film", "Rotten Tomatoes", "Metacritic", "IMDb"], ["2006", "The Boss of It All", "74%", "71%", "6.7/10"], ["2000", "Dancer in the Dark", "68%", "61%", "8.0/10"], ["1996", "Breaking the Waves", "86%", "76%", "7.9/10"], ["1998", "The Idiots", "70%", "47%", "6.9/10"], ["2003", "The Five Obstructions", "88%", "79%", "7.5/10"], ["2009", "Antichrist", "48%", "49%", "6.6/10"], ["2003", "Dogville", "70%", "59%", "8.0/10"], ["1984", "The Element of Crime", "77%", "N/A", "6.9/10"], ["2013", "Nymphomaniac: Volume I", "77%", "63%", "7.5/10"], ["1982", "Images of Liberation", "N/A", "N/A", "5.1/10"], ["1987", "Epidemic", "33%", "66%", "6.1/10"], ["2013", "Nymphomaniac: Volume II", "79%", "76%", "7.2/10"], ["2005", "Manderlay", "51%", "46%", "7.4/10"], ["2011", "Melancholia", "77%", "80%", "7.1/10"], ["1991", "Europa", "85%", "66%", "7.7/10"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the total number of films he has made? | 15 | 128 | Answer: |
Table InputTable: [["Representative", "Party", "Home Town/City", "District"], ["Jose Menendez", "D", "San Antonio", "124"], ["Roland Gutierrez", "D", "San Antonio", "119"], ["Philip Cortez", "D", "San Antonio", "117"], ["Mike Villarreal", "D", "San Antonio", "123"], ["Joe Farias", "D", "San Antonio", "118"], ["Trey Martinez Fischer", "D", "San Antonio", "116"], ["Justin Rodriguez", "D", "San Antonio", "125"], ["Joe Straus", "R", "San Antonio", "121"], ["Lyle Larson", "R", "San Antonio", "122"], ["Ruth McClendon", "D", "San Antonio", "120"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who is the last representative on the table? | Justin Rodriguez | 128 | Answer: |
Table InputTable: [["Rank", "Island", "Area\\n(km²)", "Area\\n(sq mi)", "Country/Countries/Region"], ["220", "Sheppey", "94", "36", "United Kingdom"], ["243", "Ytre Vikna (outer island of Vikna archipelago)", "83", "32", "Norway"], ["268", "Guernsey", "63", "24", "Guernsey, British Crown dependency"], ["226", "Öja (island)", "90", "35", "Finland"], ["298", "Sanday, Orkney", "50", "19", "United Kingdom"], ["217", "Borðoy", "95", "37", "Faroe Islands, an autonomous region of Denmark"], ["275", "Bolshoy Berezovy (in Beryozovye Islands, Gulf of Finland)", "60", "23", "Russia"], ["273", "Graciosa Island", "62", "24", "Portugal"], ["247", "Tiree", "78", "30", "United Kingdom"], ["272", "Raasay", "62", "24", "United Kingdom"], ["302", "Wahlbergøya", "50", "19", "Svalbard, Norway"], ["213", "Santa Maria Island", "97", "37", "Portugal"], ["222", "Lemland", "92", "36", "Finland"], ["295", "San Pietro Island", "51", "20", "Italy"], ["303", "South Ronaldsay", "50", "19", "United Kingdom"], ["253", "Hertsön", "73", "28", "Sweden"], ["289", "Hemsön", "54", "21", "Sweden"], ["221", "Gräsö", "93", "36", "Sweden"], ["223", "Vormsi", "92", "36", "Estonia"], ["260", "Finnøya (in Nordland)", "68", "26", "Norway"], ["261", "Alnön", "68", "26", "Sweden"], ["249", "Coll", "77", "30", "United Kingdom"], ["259", "Engeløya", "68", "26", "Norway"], ["258", "Ålön (in Pargas/Parainen)", "70", "27", "Finland"], ["288", "Inner-Vikna (inner island of Vikna archipelago)", "55", "21", "Norway"], ["233", "Ærø", "88", "34", "Denmark"], ["257", "Tåsinge", "70", "27", "Denmark"], ["281", "Ameland", "58", "22", "Netherlands"], ["256", "Storlandet (Finnish: Iso-Nauvo) (Nagu/Nauvo main island)", "72", "29", "Finland"], ["238", "Nordkvaløya", "84", "33", "Norway"], ["294", "Mörkön", "52", "20", "Sweden"], ["242", "Pantelleria", "83", "32", "Italy"], ["245", "Rebbenesøya", "82", "32", "Norway"], ["274", "Ljusterö", "62", "24", "Sweden"], ["224", "Rab", "91", "36", "Croatia"], ["290", "Dyrøya", "53", "20", "Norway"], ["215", "Amager", "96", "37", "Denmark"], ["232", "Terschelling", "88", "34", "Netherlands"], ["248", "Uløya", "78", "30", "Norway"], ["229", "Tustna", "89", "34", "Norway"], ["214", "Astypalaia", "97", "38", "Greece"], ["228", "Rolvsøy, Finnmark", "89", "34", "Norway"], ["276", "Leka", "60", "23", "Norway"], ["279", "Šolta", "59", "23", "Croatia"], ["297", "Hydra", "50", "19", "Greece"], ["292", "Pyhämaa (in Nystad/Uusikaupunki)", "53", "20", "Finland"], ["225", "Eckerö", "91", "36", "Finland"], ["230", "Austra", "88", "34", "Norway"], ["301", "Storøya", "50", "19", "Svalbard, Norway"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:the island of sheppey is 36 sq miles, what is the name of their country? | United Kingdom | 128 | Answer: |
Table InputTable: [["Year", "Network", "Race caller", "Color commentator", "Reporters", "Trophy Presentation"], ["1953", "CBS", "Bryan Field", "Mel Allen", "Phil Sutterfield", "Phil Sutterfield"], ["1952", "CBS", "Bryan Field", "Sam Renick", "", ""], ["1956", "CBS", "Fred Capossela", "Bryan Field", "", ""], ["1954", "CBS", "Bryan Field", "Mel Allen", "", "Bill Corum"], ["1959", "CBS", "Fred Capossela", "Bryan Field and Chris Schenkel", "", "Chris Schenkel"], ["1958", "CBS", "Fred Capossela", "Bryan Field", "", ""], ["1957", "CBS", "Fred Capossela", "Bryan Field", "", ""], ["1955", "CBS", "Fred Capossela", "Phil Sutterfield and Win Elliot", "", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the total number of times bryan field was the sole color commentator? | 3 | 128 | Answer: |
Table InputTable: [["Year", "Song", "US Hot 100", "U.S. Modern Rock", "U.S. Mainstream Rock", "Album"], ["1998", "\"The Sick\"", "-", "-", "-", "An Audio Guide To Everyday Atrocity"], ["1997", "\"One Thing\"", "-", "-", "-", "Pacifier"], ["2003", "\"Ether\"", "-", "-", "-", "Skeletons"], ["1998", "\"Breathe Out\"", "-", "-", "-", "An Audio Guide To Everyday Atrocity"], ["2001", "\"Bleeder\"", "-", "-", "32", "Violence"], ["1997", "\"Defaced\"", "-", "-", "-", "Pacifier"], ["1997", "\"Pacifier\"", "-", "-", "-", "Pacifier"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which song was on the us mainstream rock charts the most?--"one thing" or "the sick"? | "Bleeder" | 128 | Answer: |
Table InputTable: [["Season", "Age", "Overall", "Slalom", "Giant\\nSlalom", "Super G", "Downhill", "Combined"], ["2004", "17", "112", "–", "–", "51", "–", "—"], ["2005", "18", "37", "–", "27", "18", "49", "—"], ["2006", "19", "22", "–", "18", "37", "15", "—"], ["2011", "24", "Injured, did not compete", "Injured, did not compete", "Injured, did not compete", "Injured, did not compete", "Injured, did not compete", "Injured, did not compete"], ["2007", "20", "33", "–", "50", "15", "23", "—"], ["2008", "21", "38", "–", "–", "35", "13", "—"], ["2013", "26", "37", "–", "17", "28", "30", "—"], ["2009", "22", "9", "–", "40", "2", "5", "50"], ["2012", "25", "75", "–", "28", "–", "–", "—"], ["2010", "23", "28", "–", "–", "13", "23", "—"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the highest overall standing that she achieved? | 112 | 128 | Answer: |
Table InputTable: [["Year", "Film", "Role", "Language", "Notes"], ["2008", "Moggina Manasu", "Chanchala", "Kannada", "Filmfare Award for Best Actress - Kannada\\nKarnataka State Film Award for Best Actress"], ["2012", "Addhuri", "Poorna", "Kannada", "Udaya Award for Best Actress\\nNominated — SIIMA Award for Best Actress\\nNominated — Filmfare Award for Best Actress – Kannada"], ["2011", "Hudugaru", "Gayithri", "Kannada", "Nominated, Filmfare Award for Best Actress – Kannada"], ["2010", "Krishnan Love Story", "Geetha", "Kannada", "Filmfare Award for Best Actress - Kannada\\nUdaya Award for Best Actress"], ["2009", "Love Guru", "Kushi", "Kannada", "Filmfare Award for Best Actress - Kannada"], ["2009", "Olave Jeevana Lekkachaara", "Rukmini", "Kannada", "Innovative Film Award for Best Actress"], ["2013", "Bahaddoor", "Anjali", "Kannada", "Filming"], ["2012", "18th Cross", "Punya", "Kannada", ""], ["2012", "Alemari", "Neeli", "Kannada", ""], ["2012", "Drama", "Nandini", "Kannada", ""], ["2013", "Dilwala", "Preethi", "Kannada", ""], ["2010", "Gaana Bajaana", "Radhey", "Kannada", ""], ["2012", "Sagar", "Kajal", "Kannada", ""], ["2013", "Kaddipudi", "Uma", "Kannada", ""], ["2012", "Breaking News", "Shraddha", "Kannada", ""], ["2014", "Endendigu", "", "", "Filming"], ["2014", "Mr. & Mrs. Ramachari", "", "", "Announced"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the first movie that won the filmfare award for best actress? | Moggina Manasu | 128 | Answer: |
Table InputTable: [["Round", "Date", "Circuit", "Winning driver (TA2)", "Winning vehicle (TA2)", "Winning driver (TA1)", "Winning vehicle (TA1)"], ["5", "July 8", "Watkins Glen‡", "Hal Shaw, Jr.\\n Monte Shelton", "Porsche 935", "Brian Fuerstenau\\n Bob Tullius", "Jaguar XJS"], ["7", "August 19", "Mosport", "Greg Pickett", "Chevrolet Corvette", "Bob Tullius", "Jaguar XJS"], ["8", "September 4", "Road America", "Greg Pickett", "Chevrolet Corvette", "Bob Tullius", "Jaguar XJS"], ["4", "June 25", "Mont-Tremblant", "Monte Sheldon", "Porsche 935", "Bob Tullius", "Jaguar XJS"], ["1", "May 21", "Sears Point", "Greg Pickett", "Chevrolet Corvette", "Gene Bothello", "Chevrolet Corvette"], ["3", "June 11", "Portland", "Tuck Thomas", "Chevrolet Monza", "Bob Matkowitch", "Chevrolet Corvette"], ["6", "August 13", "Brainerd", "Jerry Hansen", "Chevrolet Monza", "Bob Tullius", "Jaguar XJS"], ["9", "October 8", "Laguna Seca", "Greg Pickett", "Chevrolet Corvette", "Bob Tullius", "Jaguar XJS"], ["10", "November 5", "Mexico City", "Ludwig Heimrath", "Porsche 935", "Bob Tullius", "Jaguar XJS"], ["2", "June 4", "Westwood", "Ludwig Heimrath", "Porsche 935", "Nick Engels", "Chevrolet Corvette"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:are there at least 11 rounds? | no | 128 | Answer: |
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["1", "Soviet Union", "*7*", "3", "6", "16"], ["7", "Norway", "2", "1", "1", "4"], ["5", "Sweden", "2", "4", "4", "10"], ["4", "Switzerland", "3", "2", "1", "6"], ["3", "Finland", "3", "3", "1", "7"], ["2", "Austria", "4", "3", "4", "11"], ["9", "Germany", "1", "0", "1", "2"], ["8", "Italy", "1", "2", "0", "3"], ["10", "Canada", "0", "1", "2", "3"], ["6", "United States", "2", "3", "2", "7"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many nations participated in the 1956 winter olympic games? | 10 | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Notes"], ["1999", "World Youth Championships", "Bydgoszcz, Poland", "13th (q)", "4.60 m"], ["2003", "European U23 Championships", "Bydgoszcz, Poland", "13th (q)", "5.20 m"], ["2001", "European Junior Championships", "Grosseto, Italy", "7th", "5.15 m"], ["2005", "European U23 Championships", "Erfurt, Germany", "7th", "5.50 m"], ["2006", "European Championships", "Gothenburg, Sweden", "5th", "5.65 m"], ["2012", "European Championships", "Helsinki, Finland", "–", "NM"], ["2005", "Universiade", "Izmir, Turkey", "5th", "5.50 m"], ["2010", "European Championships", "Barcelona, Spain", "3rd", "5.75 m"], ["2006", "World Indoor Championships", "Moscow, Russia", "10th (q)", "5.65 m"], ["2007", "European Indoor Championships", "Birmingham, United Kingdom", "16th (q)", "5.40 m"], ["2008", "Olympic Games", "Beijing, China", "11th", "5.45 m"], ["2002", "World Junior Championships", "Kingston, Jamaica", "8th", "5.30 m"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what year did poland achieve its highest position? | 2010 | 128 | Answer: |
Table InputTable: [["Speed", "Date", "Country", "Train", "Arr.", "Power", "State", "Comments"], ["202.6 km/h (126 mph)", "1938-07-03", "UK", "LNER Class A4 No. 4468 Mallard", "Loc", "Steam", "Unkn.", "Downhill grade. Data indicates peak speed 202.6 km/h (126 mph), mean speed (half-mile) 201.2 km/h (125 mph). Mallard suffered an overheated crankpin during the run, but was repaired and returned to traffic within 9 days."], ["164 km/h (102 mph)", "1904-05-09", "UK", "GWR 3700 Class 3440 City of Truro", "Loc", "Steam", "Unmod.", "Claimed[by whom?] to be the first steam locomotive to reach100 mph (161 km/h).[citation needed]"], ["161 km/h (100 mph)", "1934-11-30", "UK", "LNER Class A3 4472 Flying Scotsman", "Loc", "Steam", "Unmod.", "In 1934, Flying Scotsman achieved the first authenticated 100 mph (161 km/h) by a steam locomotive."], ["182.4 km/h (113 mph)", "1972-10-11", "Germany", "BR 18 201", "Loc", "Steam", "Unkn.", "The fastest operational steam locomotive as of 2011.[citation needed]"], ["180.3 km/h (112 mph)", "1935-09-29", "UK", "LNER Class A4 2509 Silver Link", "Loc", "Steam", "Unkn.", "Authenticated. Some sources say 112.5 mph.[citation needed]"], ["168.5 km/h (105 mph)", "1935-03-05", "UK", "LNER Class A3 No. 2750 Papyrus", "Loc", "Steam", "Unmod.", "First run at 100+ mph with complete, surviving documentation.[citation needed]"], ["131.6 km/h (82 mph)", "1854-06", "UK", "Bristol & Exeter Railway #41", "Loc", "Steam", "Unmod.", "Broad gauge[citation needed]"], ["8 km/h (5 mph)", "1804-02-21", "UK", "Richard Trevithick's world's first railway steam locomotive", "Loc", "Steam", "Unmod.", "[citation needed]"], ["185.07 km/h (115 mph)", "1905-06-11", "USA", "Pennsylvania Railroad E2 #7002", "Loc", "Steam", "Unmod.", "Claimed.[by whom?] Clocked at Crestline, Ohio at 127.1 mph (205 km/h) in 1905. However PRR Steam Locomotives did not carry speedometers at that time, speed was calculated by measuring time between mile markers, so this is not recognized as a speed record.[citation needed]"], ["200.4 km/h (125 mph)", "1936-05-11", "Germany", "Borsig DRG series 05 002", "Loc", "Steam", "Unkn.", "Level grade.[citation needed]"], ["145 km/h (90 mph)", "1895-08-22", "UK", "LNWR No. 790 Hardwicke", "Loc", "Steam", "Unmod.", "Maximum speed claimed[by whom?], although average speed record was authenticated.[citation needed]"], ["24 km/h (15 mph)", "1825", "UK", "Locomotion No. 1", "Loc", "Steam", "Unmod.", "[citation needed]"], ["166.6 km/h (104 mph)", "1934-07-20", "USA", "Milwaukee Road class F6 #6402", "Loc", "Steam", "Unmod", "A point between Oakwood, Illinois and Lake, Wisconsin. Also averaged 75.5 mph (122 km/h) on 85 miles (137 km) from Chicago, Illinois to Milwaukee, and 89.92 mph (145 km/h) for a 68.9 miles (110.9 km) stretch"], ["96.6 km/h (60 mph)", "1848", "USA", "Boston and Maine Railroad Antelope", "Loc", "Steam", "Unmod.", "First authenticated 60 mph (97 km/h),26 miles (42 km) in 26 minutes.[citation needed]"], ["125.6 km/h (78 mph)", "1850", "UK", "Great Britain", "Loc", "Steam", "Unmod.", "80 mph (129 km/h) claimed[by whom?][citation needed]"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many km/h did the train lner class a4 no. 4468 mallard travel? | 202.6 km/h | 128 | Answer: |
Table InputTable: [["District", "Incumbent", "Party", "First\\nelected", "Result", "Candidates"], ["Maryland 7", "Elijah Cummings", "Democratic", "1996", "Re-elected", "Elijah Cummings (D) 73.53%\\nJoseph Ward (R) 26.38%"], ["Maryland 5", "Steny Hoyer", "Democratic", "1981", "Re-elected", "Steny Hoyer (D) 69.27%\\nJoseph Crawford (R) 30.52%"], ["Maryland 6", "Roscoe Bartlett", "Republican", "1992", "Re-elected", "Roscoe Bartlett (R) 66.11%\\nDonald DeArmon (D) 33.80%"], ["Maryland 3", "Ben Cardin", "Democratic", "1986", "Re-elected", "Ben Cardin (D) 65.72%\\nScott Conwell (R) 34.18%"], ["Maryland 1", "Wayne Gilchrest", "Republican", "1990", "Re-elected", "Wayne Gilchrest (R) 76.67%\\nAnn Tamlyn (D) 23.16%"], ["Maryland 4", "Albert Wynn", "Democratic", "1992", "Re-elected", "Albert Wynn (D) 78.57%\\nJohn Kimble (R) 20.82%"], ["Maryland 2", "Robert Ehrlich", "Republican", "1994", "Retired to run for Governor\\nDemocratic gain", "Dutch Ruppersberger (D) 54.16%\\nHelen Bentley (R) 45.57%"], ["Maryland 8", "Connie Morella", "Republican", "1986", "Lost re-election\\nDemocratic gain", "Chris Van Hollen (D) 51.71%\\nConnie Morella (R) 47.49%\\nStephen Bassett (UN) 0.73%"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:congressmen re-elected with at least 60% of the vote | Wayne Gilchrest, Ben Cardin, Albert Wynn, Steny Hoyer, Roscoe Bartlett, Elijah Cummings | 128 | Answer: |
Table InputTable: [["Rank", "Name", "Nationality", "Time", "Notes", "Points"], ["2", "Jeff Lastennet", "France", "1:46.70", "", "11"], ["3", "Gareth Warburton", "Great Britain", "1:46.95", "SB", "10"], ["8", "Robin Schembera", "Germany", "1:47.79", "", "5"], ["4", "Mario Scapini", "Italy", "1:47.20", "PB", "9"], ["12", "Milan Kocourek", "Czech Republic", "1:59.28", "", "1"], ["9", "Ivan Tukhtachev", "Russia", "1:48.27", "SB", "4"], ["1", "Adam Kszczot", "Poland", "1:46.50", "", "12"], ["11", "António Rodrigues", "Portugal", "1:50.45", "", "2"], ["10", "Antonio Manuel Reina", "Spain", "1:48.56", "", "3"], ["7", "Joni Jaako", "Sweden", "1:47.61", "SB", "6"], ["6", "Oleh Kayafa", "Ukraine", "1:47.42", "", "7"], ["5", "Anis Ananenka", "Belarus", "1:47.29", "", "8"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how long did it take jeff lastennet to finish? | 1:46.70 | 128 | Answer: |
Table InputTable: [["Pos", "No.", "Driver", "Team", "Engine", "Laps", "Time/Retired", "Grid", "Laps Led", "Points"], ["1", "2", "Ryan Briscoe", "Team Penske", "Chevrolet", "85", "2:07:02.8248", "2", "27", "50"], ["18", "28", "Ryan Hunter-Reay", "Andretti Autosport", "Chevrolet", "84", "+ 1 lap", "7", "1", "12"], ["15", "17", "Sebastián Saavedra", "Andretti Autosport", "Chevrolet", "84", "+ 1 lap", "23", "0", "15"], ["13", "9", "Scott Dixon", "Chip Ganassi Racing", "Honda", "84", "+ 1 lap", "5", "0", "17"], ["21", "83", "Charlie Kimball", "Chip Ganassi Racing", "Honda", "82", "+ 3 laps", "21", "0", "12"], ["25", "26", "Marco Andretti", "Andretti Autosport", "Chevrolet", "46", "Mechanical", "12", "0", "10"], ["20", "20", "Ed Carpenter", "Ed Carpenter Racing", "Chevrolet", "84", "+ 1 lap", "25", "0", "12"], ["11", "18", "Justin Wilson", "Dale Coyne Racing", "Honda", "84", "+ 1 lap", "20", "0", "19"], ["12", "19", "James Jakes", "Dale Coyne Racing", "Honda", "84", "+ 1 lap", "24", "0", "18"], ["3", "10", "Dario Franchitti", "Chip Ganassi Racing", "Honda", "85", "+ 1.0497", "6", "0", "35"], ["22", "7", "Sebastien Bourdais", "Dragon Racing", "Chevrolet", "63", "Contact", "3", "0", "12"], ["26", "27", "James Hinchcliffe", "Andretti Autosport", "Chevrolet", "35", "Mechanical", "10", "0", "10"], ["5", "38", "Graham Rahal", "Chip Ganassi Racing", "Honda", "85", "+ 9.4667", "13", "0", "30"], ["23", "67", "Josef Newgarden (R)", "Sarah Fisher Hartman Racing", "Honda", "62", "Contact", "22", "0", "12"], ["6", "3", "Hélio Castroneves", "Team Penske", "Chevrolet", "85", "+ 11.2575", "4", "0", "28"], ["4", "8", "Rubens Barrichello", "KV Racing Technology", "Chevrolet", "85", "+ 8.8529", "11", "0", "32"], ["10", "11", "Tony Kanaan", "KV Racing Technology", "Chevrolet", "84", "+ 1 lap", "16", "0", "20"], ["16", "5", "E.J. Viso", "KV Racing Technology", "Chevrolet", "84", "+ 1 lap", "17", "0", "14"], ["24", "6", "Katherine Legge (R)", "Dragon Racing", "Chevrolet", "48", "Mechanical", "19", "0", "12"], ["17", "78", "Simona de Silvestro", "HVM Racing", "Lotus", "84", "+ 1 lap", "27", "0", "13"], ["8", "4", "J.R. Hildebrand", "Panther Racing", "Chevrolet", "85", "+ 22.8121", "15", "0", "24"], ["19", "22", "Oriol Servià", "Panther/Dreyer & Reinbold Racing", "Chevrolet", "84", "+ 1 lap", "18", "0", "12"], ["7", "77", "Simon Pagenaud (R)", "Schmidt Hamilton Motorsports", "Honda", "85", "+ 12.3087", "9", "0", "26"], ["14", "14", "Mike Conway", "A.J. Foyt Enterprises", "Honda", "84", "+ 1 lap", "14", "0", "16"], ["2", "12", "Will Power", "Team Penske", "Chevrolet", "85", "+ 0.4408", "1", "57", "43"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how long did it take ryan briscoe to finish the 2012 gopro indy grand prix of sonoma? | 2:07:02.8248 | 128 | Answer: |
Table InputTable: [["Match Day", "Date", "Opponent", "H/A", "Score", "Aberdeen Scorer(s)", "Attendance"], ["34", "20 March", "Airdrieonians", "H", "3–0", "Brewster, Cail, Main", "5,500"], ["31", "27 February", "Third Lanark", "A", "1–0", "Walker", "5,000"], ["20", "19 December", "Kilmarnock", "H", "3–0", "MacLachlan, Cail, Main", "4,000"], ["5", "12 September", "Ayr United", "A", "0–1", "", "2,000"], ["2", "22 August", "Rangers", "H", "0–2", "", "15,000"], ["30", "20 February", "Hibernian", "H", "0–0", "", "8,500"], ["29", "13 February", "St. Mirren", "A", "2–0", "Cail, Walker", "3,000"], ["24", "9 January", "Ayr United", "H", "1–1", "Cail", "4,500"], ["19", "12 December", "Partick Thistle", "A", "0–3", "", "6,000"], ["4", "5 September", "Clyde", "H", "2–0", "MacLachlan, Archibald", "6,000"], ["23", "2 January", "Raith Rovers", "A", "1–5", "Cail", "6,000"], ["32", "6 March", "Partick Thistle", "H", "0–0", "", "6,000"], ["14", "7 November", "Raith Rovers", "H", "1–3", "Main", "6,000"], ["28", "6 February", "Morton", "H", "2–0", "Brewster, Archibald", "2,000"], ["18", "5 December", "Celtic", "H", "0–1", "", "7,000"], ["25", "16 January", "Clyde", "A", "0–3", "", "3,000"], ["35", "27 March", "Rangers", "A", "1–1", "W. Wylie", "10,000"], ["13", "31 October", "Hibernian", "A", "2–1", "Chatwin, Main", "4,000"], ["27", "30 January", "Dumbarton", "A", "2–3", "Cail, Walker", "3,000"], ["9", "3 October", "St. Mirren", "H", "0–0", "", "6,000"], ["16", "21 November", "Dumbarton", "H", "0–0", "", "5,000"], ["26", "23 January", "Falkirk", "H", "1–2", "Walker", "4,000"], ["37", "10 April", "Celtic", "A", "0–1", "", "10,000"], ["3", "29 August", "Morton", "A", "1–1", "Cail", "4,500"], ["17", "28 November", "Kilmarnock", "A", "2–5", "MacLachlan, McLeod", "2,500"], ["1", "15 August", "Dundee", "A", "3–1", "Soye, Walker, Cail", "10,000"], ["6", "19 September", "Motherwell", "H", "3–1", "J. Wyllie, MacLachlan, Walker", "7,000"], ["12", "24 October", "Falkirk", "A", "1–1", "J. Wyllie", "5,500"], ["22", "1 January", "Dundee", "H", "2–1", "Walker, J. Wyllie", "7,000"], ["10", "10 October", "Airdrieonians", "A", "0–3", "", "7,000"], ["21", "26 December", "Motherwell", "A", "1–1", "Walker", "3,000"], ["11", "17 October", "Third Lanark", "H", "1–2", "Archibald", "6,000"], ["36", "3 April", "Heart of Midlothian", "H", "0–0", "", "6,000"], ["38", "17 April", "Hamilton Academical", "H", "1–0", "J. Wyllie", "4,000"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:in how many games did the team score at least 3 goals? | 4 | 128 | Answer: |
Table InputTable: [["Rank", "Diver", "Preliminary\\nPoints", "Preliminary\\nRank", "Final\\nPoints"], ["11", "Anita Rossing (SWE)", "464.58", "7", "424.98"], ["19", "Alison Childs (GBR)", "400.68", "19", ""], ["23", "Angela Ribeiro (BRA)", "370.68", "23", ""], ["24", "Rim Hassan (EGY)", "258.63", "24", ""], ["18", "Valerie McFarland-Beddoe (AUS)", "401.13", "18", ""], ["", "Christina Seufert (USA)", "481.41", "5", "517.62"], ["20", "Kerstin Finke (FRG)", "393.93", "20", ""], ["21", "Nicole Kreil (AUT)", "382.68", "21", ""], ["13", "Ann Fargher (NZL)", "421.65", "13", ""], ["15", "Antonette Wilken (ZIM)", "414.66", "15", ""], ["7", "Lesley Smith (ZIM)", "438.72", "10", "451.89"], ["17", "Claire Izacard (FRA)", "403.17", "17", ""], ["6", "Elsa Tenorio (MEX)", "460.56", "8", "463.56"], ["9", "Jennifer Donnet (AUS)", "432.78", "12", "443.13"], ["22", "Joana Figueiredo (POR)", "374.07", "22", ""], ["12", "Verónica Ribot (ARG)", "443.25", "9", "422.52"], ["5", "Li Qiaoxian (CHN)", "466.83", "6", "487.68"], ["4", "Li Yihua (CHN)", "517.92", "1", "506.52"], ["", "Kelly McCormick (USA)", "516.75", "2", "527.46"], ["14", "Tine Tollan (NOR)", "419.55", "14", ""], ["10", "Daphne Jongejans (NED)", "487.95", "4", "437.40"], ["", "Sylvie Bernier (CAN)", "489.51", "3", "530.70"], ["8", "Debbie Fuller (CAN)", "437.04", "11", "450.99"], ["16", "Guadalupe Canseco (MEX)", "411.96", "16", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the average score of the gold, silver, and bronze medalists? | 525.26 | 128 | Answer: |
Table InputTable: [["Year", "Kit Manufacturer", "Shirt Sponsor", "Back of Shirt Sponsor", "Short Sponsor"], ["1995–1996", "Matchwinner", "Empress", "", ""], ["1982–1985", "Umbro", "", "", ""], ["1986–1988", "Henson", "Duraflex", "", ""], ["1985–1986", "Umbro", "Whitbread", "", ""], ["1977–1978", "", "National Express", "", ""], ["2004–2008", "Errea", "Bence Building Merchants", "", ""], ["1991–1993", "Technik", "Gulf Oil", "", ""], ["1994–1995", "Klūb Sport", "Empress", "", ""], ["1993–1994", "Club Sport", "Gulf Oil", "", ""], ["1988–1989", "", "Gulf Oil", "", ""], ["1999–2004", "Errea", "Towergate Insurance", "", ""], ["1997–1999", "Errea", "Endsleigh Insurance", "", ""], ["2009–2011", "Errea", "Mira Showers", "PSU Technology Group", ""], ["1996–1997", "UK", "Endsleigh Insurance", "", ""], ["2011–2013", "Errea", "Mira Showers", "Barr Stadia", "Gloucestershire Echo"], ["2008–", "Errea", "Mira Showers", "", ""], ["2013–", "Errea", "Mira Showers", "Gloucestershire College", "Gloucestershire Echo"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:during what time period was there no shirt sponsers? | 1982-1985 | 128 | Answer: |
Table InputTable: [["Clerk", "Started", "Finished", "School (year)", "Previous clerkship"], ["Jerome B. Libin", "1959", "1960", "Michigan (1959)", "none"], ["Patrick F. McCartan", "1959", "1960", "Notre Dame (1959)", "none"], ["Heywood H. Davis", "1958", "1959", "Kansas (1958)", "none"], ["William C. Canby, Jr.", "1958", "1959", "Minnesota (1956)", "none"], ["Kenneth W. Dam", "1957", "1958", "Chicago (1957)", "none"], ["Alan C. Kohn", "1957", "1958", "Wash U (1955)", "none"], ["D. Lawrence Gunnels", "1961", "1962", "Wash U (1960)", "none"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who was the law clerk after jerome b. libin and patrick f. mccartan served as clerks? | D. Lawrence Gunnels | 128 | Answer: |
Table InputTable: [["Hand", "Auto", "Wind", "Athlete", "Nationality", "Birthdate", "Location", "Date"], ["", "10.90", "", "Thaddeus Bell", "United States", "28.11.1942", "Raleigh", "01.05.1988"], ["10.7", "", "", "Klaus Jürgen Schneider", "Germany", "02.03.1942", "Stuttgart", "07.07.1982"], ["", "10.95", "", "Karl Heinz Schröder", "Germany", "17.06.1939", "Hannover", "28.07.1979"], ["", "10.84", "1.8", "Erik Oostweegel", "Netherlands", "29.04.1960", "Tilburg", "10.06.2000"], ["", "10.93", "0.6", "Gilles Echevin", "France", "01.09.1948", "Grenoble", "07.05.1989"], ["10.7", "", "", "Walt Butler", "United States", "21.03.1941", "Northridge", "16.05.1981"], ["", "10.26", "2.3", "Troy Douglas", "Netherlands", "30.11.1962", "Utrecht", "10.07.2004"], ["", "10.29", "1.9", "Troy Douglas", "Netherlands", "30.11.1962", "Leiden", "07.06.2003"], ["", "10.87", "", "Eddie Hart", "United States", "24.04.1949", "Eugene", "03.08.1989"], ["10.7", "", "", "Thane Baker", "United States", "04.10.1931", "Elkhart", "13.09.1972"], ["", "10.60", "", "Bill Collins", "United States", "20.11.1950", "", "06.06.1992"], ["", "10.95", "", "George McNeill", "United Kingdom", "19.02.1947", "Melbourne", "31.11.1987"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who is older, thaddeus bell or klaus jürgen schneider? | Klaus Jürgen Schneider | 128 | Answer: |
Table InputTable: [["Competition", "Total spectatorship", "Average match attendance", "Year"], ["State of Origin series", "186,607", "62,202", "2011"], ["Rugby Championship", "133,532", "44,511", "2012"], ["Super Rugby", "773,940", "19,348", "2012"], ["National Rugby League", "3,345,248", "16,643", "2013"], ["Australian Football League", "6,931,085", "33,484", "2013"], ["Big Bash League", "550,262", "17,750", "2011/2012"], ["A-League", "1,772,133", "12,707", "2012/2013"], ["Women's National Basketball League", "77,944", "", "2010/2011"], ["National Basketball League", "547,021", "4,031", "2010/2011"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which had more spectators? rugby championship or state or origin series? | State of Origin series | 128 | Answer: |
Table InputTable: [["Season", "Competition", "Round", "Club", "Home", "Away"], ["2012–13", "UEFA Europa League", "1QR", "Budapest Honvéd", "0–1", "0–2"], ["", "", "2QR", "FK Jablonec 97", "0–2", "1–5"], ["2009–10", "UEFA Europa League", "2QR", "Motherwell", "1–0", "1–8"], ["1996–97", "UEFA Cup Winners' Cup", "QR", "Humenné", "0–2", "0–1"], ["2011–12", "UEFA Europa League", "1QR", "FK Budućnost", "1–2", "3–1"], ["1986–87", "UEFA Cup", "1R", "FC Barcelona", "1–1", "0–0"], ["1987–88", "UEFA Cup", "2R", "Wismut Aue", "2–0", "0–1"], ["1987–88", "UEFA Cup", "1/16", "FC Barcelona", "1–0", "1–4"], ["1990–91", "UEFA Cup Winners' Cup", "1R", "Olympiacos Piraeus", "0–2", "1–3"], ["1988–89", "UEFA Cup Winners' Cup", "1R", "Lech Poznań", "2–3", "0–1"], ["1985–86", "UEFA Cup Winners' Cup", "1R", "HJK Helsinki", "1–2", "2–3"], ["1991–92", "UEFA European Cup", "1R", "IFK Göteborg", "1–1", "0–0"], ["1987–88", "UEFA Cup", "1R", "FK Partizan Beograd", "2–0", "1–2"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the last season played? | 2012-13 | 128 | Answer: |
Table InputTable: [["Date", "Festival", "Location", "Awards", "Link"], ["Sep 16", "Athens International Film Festival", "Athens, Attica\\n Greece", "Best Director", "aiff.gr"], ["Sep 19", "Lund International Fantastic Film Festival", "Lund, Skåne\\n Sweden", "", "fff.se"], ["Oct 9", "London Int. Festival of Science Fiction Film", "London, England\\n UK", "Closing Night Film", "Sci-Fi London"], ["Feb 2–5, Feb 11", "Santa Barbara International Film Festival", "Santa Barbara, California USA", "Top 11 \"Best of the Fest\" Selection", "sbiff.org"], ["Oct 9, Oct 11", "Sitges Film Festival", "Sitges, Catalonia\\n Spain", "", "Sitges Festival"], ["May 21–22, Jun 11", "Seattle International Film Festival", "Seattle, Washington USA", "", "siff.net"], ["Nov 12, Nov 18", "Indonesia Fantastic Film Festival", "Jakarta, Bandung\\n Indonesia", "", "inaff.com"], ["Jul 18, Jul 25", "Fantasia Festival", "Montreal, Quebec Canada", "Special Mention\\n\"for the resourcefulness and unwavering determination by a director to realize his unique vision\"", "FanTasia"], ["Oct 23", "Toronto After Dark", "Toronto, Ontario\\n Canada", "Best Special Effects\\nBest Musical Score", "torontoafterdark.com"], ["Sep 28", "Fantastic Fest", "Austin, Texas\\n USA", "", "FantasticFest.com"], ["Nov 11", "Les Utopiales", "Nantes, Pays de la Loire\\n France", "", "utopiales.org"], ["Nov 16–18", "AFF", "Wrocław, Lower Silesia\\n Poland", "", "AFF Poland"], ["Oct 1, Oct 15", "Gwacheon International SF Festival", "Gwacheon, Gyeonggi-do\\n South Korea", "", "gisf.org"], ["Oct 17, Oct 20", "Icon TLV", "Tel Aviv, Central\\n Israel", "", "icon.org.il"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the total number of awards this film won? | 4 | 128 | Answer: |
Table InputTable: [["No.", "Date", "Tournament", "Surface", "Partnering", "Opponent in the final", "Score"], ["7.", "August 26, 2012", "Ecuador F3", "Clay", "Sergio Galdós", "Mauricio Echazú\\n Guillermo Rivera-Aránguiz", "6-2, 6-1"], ["1.", "September 13, 2010", "Ecuador F2", "Hard", "Roberto Quiroz", "Peter Aarts\\n Christopher Racz", "6–4, 6–4"], ["6.", "August 20, 2012", "Colombia F2", "Clay", "Ariel Behar", "Nicolas Barrientos\\n Michael Quintero", "2-1 Ret."], ["2.", "April 4, 2011", "Chile F2", "Clay", "Sergio Galdós", "Guillermo Hormazábal\\n Rodrigo Pérez", "5–7, 7–6(5), [10–5]"], ["10.", "May 27, 2013", "Argentina F8", "Clay", "Sergio Galdós", "Daniel Dutra da Silva\\n Pablo Galdón", "6-0, 7-5"], ["3.", "April 11, 2011", "Chile F3", "Clay", "Roberto Quiroz", "Luis David Martínez\\n Miguel Ángel Reyes-Varela", "6–4, 7–5"], ["9.", "May 13, 2013", "Argentina F6", "Clay", "Sergio Galdós", "Franco Agamenone\\n Jose Angel Carrizo", "4-6, 6-4, [10–1]"], ["8.", "October 8, 2012", "Chile F8", "Clay", "Gustavo Sterin", "Cristóbal Saavedra-Corvalán\\n Guillermo Rivera-Aránguiz", "6-4, 7-5"], ["5.", "August 5, 2012", "Manta", "Hard", "Renzo Olivo", "Víctor Estrella\\n João Souza", "6–3, 6–0"], ["4.", "August 8, 2011", "Peru F1", "Clay", "Sergio Galdós", "Martín Cuevas\\n Guido Pella", "6–4, 6–0"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what tournament comes after ecuador f2? | Chile F2 | 128 | Answer: |
Table InputTable: [["Common name", "Binomial nomenclature", "Colour", "Density ¹", "Location", "Characteristics, Usage and Status"], ["Deodar", "Cedrus deodara", "Yellowish brown", "560 kg/m³", "Himalayas, Punjab, Uttar Pradesh", "Deodar is the most important timber tree providing soft wood. It can be easily worked and it is moderately strong. It possesses distinct annual rings. It is used for making cheap furniture, railway carriages, railway sleepers, packing boxes, structural work and so forth."], ["Axlewood", "Anogeissus latifolia", "", "930 kg/m³", "Andhra Pradesh, Tamil Nadu, Maharashtra, Madhya Pradesh, Bihar, Uttar Pradesh", "It is very strong, hard and tough. It takes a smooth finish. It is subject to cracking."], ["Tamarind", "Tamarindus indica", "Dark brown", "1280 kg/m³[citation needed]", "All over India", "Tamarind is knotty and durable. It is a beautiful tree for avenue and gardens. Its development is very slow but it ultimately forms a massive appearance. Its fruit is also very useful. It is used for agricultural instruments, well curbs, sugar mills, carts and brick burning."], ["Mango", "Mangifera spp", "Deep gray", "560–720 kg/m³", "Throughout India", "The mango tree is well known for its fruits. It is easy to work and it maintains its shape well. It is moderately strong. It is most often used for cheap furniture, toys, packing boxes, cabinet work, panels for doors and for windows."], ["Bamboo", "Family Poaceae, tribe Bambuseae", "", "", "Throughout India, especially Assam and Bengal", "Not actually a tree, but a woody grass, it is flexible, very strong and durable. It is used for scaffoldings, thatched roofs, rafters, temporary bridges, and so forth."], ["Arjun", "Terminalia arjuna Terminalia elliptica", "Dark brown", "870 kg/m³", "Central India", "It is heavy and strong. It has such uses as beams, rafters, and posts."], ["Teak", "Tectona grandis", "Deep yellow to dark brown", "639 kg/m³", "Central India and Southern India", "Moderately hard, teak is durable and fire-resistant. It can be easily seasoned and worked. It takes up a good polish and is not attacked by white ants and dry rot. It does not corrode iron fastenings and it shrinks little. It is among the most valuable timber trees of the world and its use is limited to superior work only."], ["Banyan", "Ficus benghalensis", "Brown", "580 kg/m³", "Throughout India", "It is strong and durable only under water. The aerial roots are utilized for such items as tent poles and well curbs."], ["Casuarina", "Casuarina spp.", "Reddish brown", "765 kg/m³", "Andhra Pradesh, Tamil Nadu", "It grows straight. It is strong and fibrous. It is, however, badly twisted. It is often used for scaffolding and posts for temporary structures."], ["Satinwood", "Chloroxylon swietenia", "Yellow", "960 kg/m³", "Central and Southern India", "It is very hard and durable. It is close grained. It is used for furniture and other ornamental works. Vulnerable"], ["Sandalwood", "Santalum spp.", "White or Red", "930 kg/m³", "Karnataka, Tamil Nadu, Kerala, Assam, Nagpur, Bengal", "It has a pleasant smell. It is commonly used for agricultural instruments, well curbs, wheels, and mallets. Vulnerable"], ["Palm", "Arecaceae", "Dark brown", "1040 kg/m³", "Throughout India", "It contains ripe wood in the outer crust. The colour of this ripened wood is dark brown. It is strong, durable and fibrous. Palm is used for furniture, roof covering, rafters and joists."], ["Irul, Pyinkado", "Xylia xylocarpa", "", "830–1060 kg/m³", "Karnataka, Kerala, Andhra Pradesh, Maharashtra, Orissa, Tamil Nadu", "It is very hard, heavy and durable. Difficult to work, it also requires slow and careful seasoning. It is used for railway sleepers, agricultural instruments, paving blocks, and heavy construction. Least concern"], ["Himalayan Elm, Indian Elm", "Ulmus wallichiana", "Red", "960 kg/m³", "Throughout India", "It is moderately hard and strong. It is used for door and window frames, carts, and so forth."]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what tree is the most dense in india? | Tamarind | 128 | Answer: |
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["8", "China", "–", "–", "1", "1"], ["7", "Russia", "–", "1", "1", "2"], ["6", "Italy", "–", "2", "1", "3"], ["1", "Netherlands", "8", "3", "1", "12"], ["2", "Australia", "3", "3", "4", "10"], ["5", "Canada", "1", "–", "3", "4"], ["3", "United States", "2", "5", "1", "8"], ["4", "Hungary", "1", "1", "3", "5"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who is at the top of the list? | Netherlands | 128 | Answer: |
Table InputTable: [["Num", "Pod Color", "Nickname", "Short Description", "Episode"], ["071", "Yellow", "Penny", "Seen in pod form in Stitch! The Movie. Pod says 71 instead of 071. Function unknown.", "Leroy & Stitch"], ["078", "White", "Snozzle", "Seen in pod form in Stitch! The Movie. Pod says 78 instead of 078. Function unknown.", "Leroy & Stitch"], ["019", "White", "Clumsy", "Seen in pod form in Stitch! The Movie. Pod says 19 instead of 019. Function unknown.", "Leroy & Stitch"], ["070", "White", "Flapjack", "Seen in pod form in Stitch! The Movie. Function unknown. Pod says 70 instead of 070.", "Stitch! The Movie"], ["094", "White", "Louis B.", "Seen in pod form in Stitch! The Movie. Function unknown. Pod says 94 instead of 094.", "Stitch! The Movie"], ["082", "White", "Plunge", "Seen in pod form in Stitch! The Movie. Function unknown. Pod says 82 instead of 082.", "Stitch! The Movie"], ["074", "White", "Welco", "This experiment was seen in pod form in Stitch! The Movie. Pod says 74 instead of 074.", "Stitch! The Movie"], ["008", "Orange/Brown", "Carmine", "", "Leroy & Stitch"], ["011", "Green", "Inkstain", "", "Leroy & Stitch"], ["028", "White", "Lori", "", "leroy and stitch"], ["061", "", "Anachronator", "", "Leroy & Stitch"], ["002", "Purple", "Doubledip", "A purple opossum-like experiment with two light purple stripes on the back of his ears, beady eyes and an orange nose (In Leroy & Stitch, his nose is dark purple). Designed to double-dip food. His one true place is with Mrs. Hasagawa as one of her \"cats\". He somehow changed in size in Leroy & Stitch.", "220, Leroy & Stitch"], ["072", "", "Stickystuck", "", "Leroy & Stitch"], ["099", "", "Spot", "A spotlight experiment that was supposed to be in \"Spike\" but was removed, and was supposed to appear later. He shines a spotlight on people. Looks like Heat. Seen in \"Leroy and Stitch\".", "Leroy & Stitch"], ["091", "", "Nutsy", "", "Leroy & Stitch"], ["064", "", "Nappifier", "", "Leroy & Stitch"], ["079", "", "Fogger", "", "Leroy & Stitch"], ["080", "", "Dan", "", "Leroy & Stitch"], ["081", "", "Backscratcher", "", "Leroy & Stitch"], ["049", "White", "Picker", "This experiment was seen in pod form in Stitch! The Movie. His pod says 49 instead of 049, possibly due to the angle. Function unknown.", "Stitch! The Movie"], ["063", "", "Pufferizer", "", "Leroy & Stitch"], ["037", "", "Snipper", "", "Leroy & Stitch"], ["039", "", "Atlas", "", "Leroy & Stitch"], ["047", "", "Lorider", "", "Leroy & Stitch"], ["005", "", "Truxx", "", "Leroy & Stitch"], ["068", "", "Tom", "", "Leroy & Stitch"], ["098", "", "Cooper", "", "Leroy & Stitch"], ["006", "", "Percy", "", "Leroy & Stitch"], ["088", "", "Decrisper", "", "Leroy & Stitch"], ["069", "", "H. T.", "", "Leroy & Stitch"], ["004", "", "Squawk", "", "Leroy & Stitch"], ["041", "", "Kitsch", "", "Leroy & Stitch"], ["048", "", "Echo", "", "Leroy & Stitch"], ["073", "", "Cornerpiece", "", "Leroy & Stitch"], ["083", "", "Grimple", "", "Leroy & Stitch"], ["051", "Green", "Hocker", "A green experiment with a huge blue nose and a yellow spot around his eyes and a yellow stripe on his ears and tail (In his episode the spots and stripes were originally red.). Designed to spit acidic saliva that can burn through wood in about three seconds. His one true place is with Mrs. Hasagawa as one of her \"cats.\"", "220, Leroy & Stitch"], ["038", "", "Plats", "", "Leroy & Stitch"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the first pod color in the list of leroy & stitch episode? | Blue | 128 | Answer: |
Table InputTable: [["Name", "Owner", "Location", "Notes", "Transmission", "Website"], ["Radio 24", "Il Sole 24 Ore", "Milan", "Commercial; News/Talk", "FM, DAB, DVB-S", "http://www.radio24.it"], ["RadioRadio", "", "Rome", "Local; News/Talk", "DAB, DVB-S", "http://www.radioradio.it"], ["Radio Monte Carlo", "Gruppo Finelco", "Milan", "Commercial; It is also called RMC", "FM, DVB-S", "http://www.radiomontecarlo.net"], ["Radio Italia Solo Musica Italiana", "Gruppo Radio Italia", "Milan", "Commercial; Italian Hits", "FM, DAB, DVB-S", "http://www.radioitalia.it"], ["Radio Radicale", "Radical Party", "Rome", "Community; News/Talk", "FM, DAB, DVB-S", "http://www.radioradicale.it"], ["Radio Dimensione Suono", "", "Rome", "Commercial; It is also called RDS", "FM, DAB, DAB+, DVB-S", "http://www.rds.it"], ["Radio Popolare", "cooperative", "Rome", "Community; News/Talk", "FM", "http://www.radiopopolare.it"], ["Radio 105 Network", "Gruppo Finelco", "Milan", "Commercial; Rock, Pop, Hip Hop", "FM, DVB-S", "http://www.105.net"], ["Virgin Radio Italia", "Gruppo Finelco", "Milan", "Commercial; Rock", "FM, DAB, DAB+, DVB-S", "http://www.virginradioitaly.it"], ["Rai Radio 3", "RAI", "Rome", "Public; Culture; Classical music", "FM, DAB, DVB-T, DVB-S", "http://www.radio3.rai.it"], ["Rai Radio 1", "RAI", "Rome", "Public; News/Talk; Generalist", "FM, MW, DAB, DVB-T, DVB-S", "http://www.radio1.rai.it"], ["R101", "Monradio", "Milan", "Commercial; Classic hits", "FM, DAB, DAB+, DVB-S", "http://www.r101.it"], ["Rai GR Parlamento", "RAI", "Rome", "Public; News/Talk", "FM, DVB-S", "http://www.grparlamento.rai.it"], ["Radio Maria", "Associazione Radio Maria", "Erba, (CO)", "Community; Catholic", "FM, DAB, DVB-S", "http://www.radiomaria.it"], ["Multiradio", "Multiradio srl", "Massafra, (TA)", "Local; Adult Contemporary", "FM", "http://www.multiradio.it"], ["Radio Capital", "Elemedia", "Cusano Milanino", "Commercial; Classic hits", "FM, DAB, DVB-T, DVB-S", "http://www.capital.it"], ["Radio Padania Libera", "Lega Nord", "Varese", "Community; News/Talk", "DAB, DVB-S", "http://www.radiopadania.info"], ["Rai Radio 2", "RAI", "Rome", "Public; Popular music; Entertainment", "FM, DAB, DVB-T, DVB-S", "http://www.radio2.rai.it"], ["Radio Pianeta", "", "Cividate al piano. (BG)", "Local; News/Talk", "FM", "http://www.radiopianeta.it"], ["m2o", "Elemedia", "Rome", "Commercial; Electronic dance music", "FM, DAB, DAB+, DVB-T, DVB-S", "http://www.m2o.it"], ["Radio Bruno", "Radio Bruno", "Carpi (MO)", "Local; Pop, Contemporary", "FM, streaming online, Dvb-T", "http://www.radiobruno.it"], ["Radio DeeJay", "Elemedia", "Milan", "Commercial;", "FM, DAB, DAB+, DVB-T, DVB-S", "http://www.deejay.it"], ["Rai FD4 Leggera", "RAI", "Rome", "Public; Easy listening music", "DAB, Cable, DVB-T, DVB-S", "http://www.radio.rai.it/radiofd4"], ["Rai FD5 Auditorium", "RAI", "Rome", "Public; Classical music", "DAB, Cable, DVB-T, DVB-S", "http://www.radio.rai.it/radiofd5"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many more radio stations are located in rome than in milan? | 3 | 128 | Answer: |
Table InputTable: [["Contestant", "Original Tribe", "Switched Tribe", "Merged Tribe", "Finish", "Total Votes"], ["Aleksandr Pashutin\\n60.the actor", "Barracudas", "", "", "3rd Voted Out\\nDay 9", "7"], ["Kris Kelmi\\n47.the singer", "Barracudas", "", "", "2nd Voted Out\\nDay 6", "1"], ["Aleksandr Lykov\\n41.the actor", "Barracudas", "Barracudas", "Crocodiles", "13th Voted Out\\n8th Jury Member\\nDay 37", "6"], ["Aleksandr Byalko\\n50.the physicist", "Pelicans", "Barracudas", "", "5th Voted Out\\nDay 15", "6"], ["Ivar Kalnynsh\\n54.the actor", "", "", "Crocodiles", "10th Voted Out\\n5th Jury Member\\nDay 30", "3"], ["Larisa Verbitskaya\\n43.the TV presenter", "Barracudas", "Pelicans", "Crocodiles", "12th Voted Out\\n7th Jury Member\\nDay 36", "11"], ["Vera Glagoleva\\n46.the actress", "", "", "Crocodiles", "11th Voted Out\\n6th Jury Member\\nDay 33", "4"], ["Viktor Gusev\\n47.the sport commentator", "Pelicans", "Pelicans", "Crocodiles", "7th Voted Out\\n1st Jury Member\\nDay 21", "6"], ["Yelena Proklova\\n49.the TV presenter", "Pelicans", "Barracudas", "Crocodiles", "8th Voted Out\\n3rd Jury Member\\nDay 24", "4"], ["Olga Orlova\\n25.the singer", "Barracudas", "Baracudas", "Crocodiles", "Eliminated\\n9th Jury Member\\nDay 38", "10"], ["Ivan Demidov\\n39.the TV presenter", "Barracudas", "Pelicans", "Crocodiles", "Eliminated\\n2nd Jury Member\\nDay 23", "3"], ["Tatyana Dogileva\\n45.the actress", "Pelicans", "Barracudas", "", "6th Voted Out\\nDay 18", "3"], ["Marina Aleksandrova\\n20.the actress", "Barracudas", "Pelicans", "Crocodiles", "9th Voted Out\\n4th Jury Member\\nDay 27", "6"], ["Dana Borisova\\n26.the TV presenter", "Pelicans", "Barracudas", "", "4th Voted Out\\nDay 12", "5"], ["Yelena Kondulaynen\\n44.the actress", "Pelicans", "", "", "1st Voted Out\\nDay 3", "5"], ["Igor' Livanov\\n49.the actor", "Pelicans", "", "", "Eliminated\\nDay 11", "0"], ["Vladimir Presnyakov, Jr.\\n34.the singer", "Pelicans", "Pelicans", "Crocodiles", "Sole Survivor", "6"], ["Yelena Perova\\n26.the singer", "Pelicans", "Pelicans", "Crocodiles", "Runner-Up", "2"], ["Tat'yana Ovsiyenko\\n36.the singer", "Barracudas", "Pelicans", "", "Eliminated\\nDay 19", "1"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who had the most total votes? | Larisa Verbitskaya 43.the TV presenter | 128 | Answer: |
Table InputTable: [["Date", "Rnd", "Race Name", "Circuit", "City/Location", "Pole position", "Winning driver", "Winning team", "Report"], ["10", "August 5", "Marlboro 500", "Michigan International Speedway", "Brooklyn, Michigan", "Emerson Fittipaldi", "Al Unser, Jr.", "Galles-Kraco Racing", "Report"], ["9", "July 22", "Molson Indy Toronto", "Exhibition Place", "Toronto, Ontario", "Danny Sullivan", "Al Unser, Jr.", "Galles-Kraco Racing", "Report"], ["11", "August 26", "Texaco/Havoline Grand Prix of Denver", "Streets of Denver", "Denver, Colorado", "Teo Fabi", "Al Unser, Jr.", "Galles-Kraco Racing", "Report"], ["12", "September 2", "Molson Indy Vancouver", "Streets of Vancouver", "Vancouver, British Columbia", "Michael Andretti", "Al Unser, Jr.", "Galles-Kraco Racing", "Report"], ["16", "October 21", "Champion Spark Plug 300K", "Laguna Seca Raceway", "Monterey, California", "Danny Sullivan", "Danny Sullivan", "Team Penske", "Report"], ["4", "June 3", "Miller Genuine Draft 200", "Milwaukee Mile", "West Allis, Wisconsin", "Rick Mears", "Al Unser, Jr.", "Galles-Kraco Racing", "Report"], ["3", "May 27", "74th Indianapolis 500", "Indianapolis Motor Speedway", "Speedway, Indiana", "Emerson Fittipaldi", "Arie Luyendyk", "Doug Shierson Racing", "Report"], ["2", "April 22", "Toyota Long Beach Grand Prix", "Streets of Long Beach", "Long Beach, California", "Al Unser, Jr.", "Al Unser, Jr.", "Galles-Kraco Racing", "Report"], ["NC", "October 6", "Marlboro Challenge", "Nazareth Speedway", "Nazareth, Pennsylvania", "Michael Andretti", "Rick Mears", "Team Penske", "Report"], ["14", "September 23", "Texaco/Havoline 200", "Road America", "Elkhart Lake, Wisconsin", "Danny Sullivan", "Michael Andretti", "Newman/Haas Racing", "Report"], ["13", "September 16", "Red Roof Inns 200", "Mid-Ohio Sports Car Course", "Lexington, Ohio", "Michael Andretti", "Michael Andretti", "Newman/Haas Racing", "Report"], ["1", "April 8", "Autoworks 200", "Phoenix International Raceway", "Phoenix, Arizona", "Rick Mears", "Rick Mears", "Team Penske", "Report"], ["15", "October 7", "Bosch Spark Plug Grand Prix", "Nazareth Speedway", "Nazareth, Pennsylvania", "Bobby Rahal", "Emerson Fittipaldi", "Team Penske", "Report"], ["8", "July 15", "Marlboro Grand Prix at the Meadowlands", "Meadowlands Sports Complex", "East Rutherford, New Jersey", "Michael Andretti", "Michael Andretti", "Newman/Haas Racing", "Report"], ["5", "June 17", "Valvoline Grand Prix of Detroit", "Streets of Detroit", "Detroit, Michigan", "Michael Andretti", "Michael Andretti", "Newman/Haas Racing", "Report"], ["6", "June 24", "Budweiser/G.I.Joe's 200", "Portland International Raceway", "Portland, Oregon", "Danny Sullivan", "Michael Andretti", "Newman/Haas Racing", "Report"], ["7", "July 8", "Budweiser Grand Prix of Cleveland", "Cleveland Burke Lakefront Airport", "Cleveland, Ohio", "Rick Mears", "Danny Sullivan", "Team Penske", "Report"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:did galles-kraco racing have more/less than 10 wins in the season? | less | 128 | Answer: |
Table InputTable: [["Year", "Chassis", "Engine", "Start", "Finish", "Team"], ["2006", "Dallara", "Honda", "2", "25", "Team Penske"], ["2005", "Dallara", "Toyota", "5", "9", "Team Penske"], ["2007", "Dallara", "Honda", "1", "3", "Team Penske"], ["2009", "Dallara", "Honda", "1", "1", "Team Penske"], ["2008", "Dallara", "Honda", "4", "4", "Team Penske"], ["2003", "Dallara", "Toyota", "1", "2", "Team Penske"], ["2010", "Dallara", "Honda", "1", "9", "Team Penske"], ["2011", "Dallara", "Honda", "16", "17", "Team Penske"], ["2004", "Dallara", "Toyota", "8", "9", "Team Penske"], ["2001", "Dallara", "Oldsmobile", "11", "1", "Team Penske"], ["2002", "Dallara", "Chevrolet", "13", "1", "Team Penske"], ["2012", "Dallara", "Chevrolet", "6", "10", "Team Penske"], ["2013", "Dallara", "Chevrolet", "8", "6", "Team Penske"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many years was honda used in a row after 2005? | 6 | 128 | Answer: |
Table InputTable: [["Ship", "Type of Vessel", "Lake", "Location", "Lives lost"], ["Leafield", "Steamer", "Lake Superior", "", "all hands"], ["Plymouth", "Barge", "Lake Michigan", "", "7 lost"], ["Henry B. Smith", "Steamer", "Lake Superior", "", "all hands"], ["Lightship No. 82", "Lightship", "Lake Erie", "Point Albino (near Buffalo)", "6 lost"], ["Hydrus", "Steamer", "Lake Huron", "near Lexington, Michigan", "28 lost"], ["Argus", "Steamer", "Lake Huron", "25 miles off Kincardine, Ontario", "25 lost"], ["Charles S. Price", "Steamer", "Lake Huron", "near Port Huron, Michigan", "28 lost"], ["Regina", "Steamer", "Lake Huron", "near Harbor Beach, Michigan", ""], ["James Carruthers", "Steamer", "Lake Huron", "near Kincardine", "18 lost"], ["Issac M. Scott", "Steamer", "Lake Huron", "near Port Elgin, Ontario", "28 lost"], ["John A. McGean", "Steamer", "Lake Huron", "near Goderich, Ontario", "28 lost"], ["Wexford", "Steamer", "Lake Huron", "north of Grand Bend, Ontario", "all hands"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what type of vessel is the last ship? | Lightship | 128 | Answer: |
Table InputTable: [["Hull", "Type", "Original Name", "Original Operator", "Delivery", "Disposition (2012)", "2nd name", "2nd operator", "3rd Name", "3rd operator"], ["№ 7", "929-117", "Unicorn", "Kyusyu Shosen Co. Ltd.", "Oct 1990", "Active", "Pegasus 2", "Kyusyu Shosen Co. Ltd.", "", ""], ["№ 2", "929-117", "Pegasus", "Kyusyu Shosen Co. Ltd.", "Jun 1989", "Active", "Toppy 1", "Tane Yaku Jetfoils", "", ""], ["№ 4", "929-117", "Princess Dacil", "Trasmediterranea", "Mar 1990", "Active", "Pegasus", "Kyusyu Shosen Co. Ltd.", "", ""], ["№ 8", "929-117", "Beetle 2", "JR Kyushu Jet Ferries", "Feb 1991", "Active", "", "", "", ""], ["№ 12", "929-117", "Toppy 2", "Tane Yaku Jetfoils", "Apr 1992", "Active", "", "", "", ""], ["№ 3", "929-117", "Toppy 1", "Tane Yaku Jetfoils", "Sep 1989", "Active", "Beetle 3", "JR Kyushu Jet Ferries", "", ""], ["№ 15", "929-117", "Emerald Wing", "Kaijo Access Co.", "Jun 1994", "Active", "2004 Rocket 1", "Cosmo Line", "-", "Tane Yaku Jetfoil"], ["№ 5", "929-117", "Nagasaki", "JR Kyushu Jet Ferries", "Apr 1990", "Active", "Beetle 1", "JR Kyushu Jet Ferries", "", ""], ["№ 6", "929-117", "Beetle", "JR Kyushu Jet Ferries", "Jul 1990", "Active", "Rocket", "Cosmo Line", "Rocket 3", "Tane Yaku Jetfoils"], ["№ 13", "929-117", "Toppy 3", "Tane Yaku Jetfoils", "Mar 1995", "Active", "", "", "", ""], ["№ 14", "929-117", "Crystal Wing", "Kaijo Access Co.", "Jun 1994", "Active", "2002 Beetle 5", "JR Kyushu Jet Ferries", "", ""], ["№ 9", "929-117", "Venus", "Kyushu Yusen", "Mar 1991", "Active", "", "", "", ""], ["№ 11", "929-117", "Princess Teguise", "Trasmediterranea", "Jun 1991", "Active", "2007 Toppy 5", "Tane Yaku Jetfoils", "", ""], ["№ 10", "929-117", "Suisei", "Sado Kisen", "Apr 1991", "Active", "", "", "", ""], ["№ 1", "929-117", "Tsubasa", "Sado Kisen", "Mar 1998", "Active", "", "", "", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who was the second operator to work on the pegasus | Tane Yaku Jetfoils | 128 | Answer: |
Table InputTable: [["Date", "Opponent#", "Rank#", "Site", "TV", "Result", "Attendance"], ["September 17", "at Arkansas", "#12", "Razorback Stadium • Fayetteville, AR", "ABC", "W 13–6", "52,089"], ["November 19", "#6 Auburn", "#4", "Legion Field • Birmingham, AL (Iron Bowl)", "ABC", "W 21–14", "83,091"], ["January 2, 1995", "vs. #13 Ohio State*", "#6", "Citrus Bowl • Orlando, FL (Florida Citrus Bowl)", "ABC", "W 24–17", "71,195"], ["October 22", "Ole Miss", "#8", "Bryant–Denny Stadium • Tuscaloosa, AL (Rivalry)", "ABC", "W 21–10", "70,123"], ["November 12", "at #20 Mississippi State", "#6", "Scott Field • Starkville, MS (Rivalry)", "ABC", "W 29–25", "41,358"], ["December 3", "vs. #6 Florida", "#3", "Georgia Dome • Atlanta, GA (SEC Championship Game)", "ABC", "L 23–24", "74,751"], ["September 10", "Vanderbilt", "#11", "Bryant–Denny Stadium • Tuscaloosa, AL", "JPS", "W 17–7", "70,123"], ["October 8", "Southern Miss*", "#11", "Bryant–Denny Stadium • Tuscaloosa, AL", "", "W 14–6", "70,123"], ["October 1", "Georgia", "#11", "Bryant–Denny Stadium • Tuscaloosa, AL", "ESPN", "W 29–28", "70,123"], ["November 5", "at LSU", "#6", "Tiger Stadium • Baton Rouge, LA (Rivalry)", "ESPN", "W 35–17", "75,453"], ["September 24", "Tulane*", "#11", "Legion Field • Birmingham, AL", "", "W 20–10", "81,421"], ["September 3", "Tennessee–Chattanooga*", "#11", "Legion Field • Birmingham, AL", "", "W 42–13", "82,109"], ["October 15", "at Tennessee", "#10", "Neyland Stadium • Knoxville, TN (Third Saturday in October)", "ESPN", "W 17–13", "96,856"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many games played on abc? | 6 | 128 | Answer: |
Table InputTable: [["Competition", "Total spectatorship", "Average match attendance", "Year"], ["Super Rugby", "773,940", "19,348", "2012"], ["National Rugby League", "3,345,248", "16,643", "2013"], ["Rugby Championship", "133,532", "44,511", "2012"], ["Australian Football League", "6,931,085", "33,484", "2013"], ["Big Bash League", "550,262", "17,750", "2011/2012"], ["A-League", "1,772,133", "12,707", "2012/2013"], ["State of Origin series", "186,607", "62,202", "2011"], ["National Basketball League", "547,021", "4,031", "2010/2011"], ["Women's National Basketball League", "77,944", "", "2010/2011"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which competition has more average match attendance, super rugby or national rugby league? | Super Rugby | 128 | Answer: |
Table InputTable: [["Pos", "Name", "Team", "Laps", "Time/Retired", "Grid", "Points"], ["11", "Dan Clarke", "Minardi Team USA", "69", "+38.903", "10", "11"], ["6", "Simon Pagenaud", "Team Australia", "69", "+22.698", "5", "19"], ["13", "Robert Doornbos", "Minardi Team USA", "69", "+1:00.638", "9", "8"], ["1", "Justin Wilson", "RuSPORT", "69", "1:46:02.236", "2", "32"], ["2", "Jan Heylen", "Conquest Racing", "69", "+7.227", "7", "27"], ["3", "Bruno Junqueira", "Dale Coyne Racing", "69", "+8.419", "11", "26"], ["14", "Will Power", "Team Australia", "69", "+1:01.204", "8", "7"], ["15", "Alex Tagliani", "Rocketsports", "68", "+ 1 Lap", "12", "6"], ["4", "Tristan Gommendy", "PKV Racing", "69", "+9.037", "3", "23"], ["9", "Graham Rahal", "N/H/L Racing", "69", "+23.949", "6", "13"], ["16", "Alex Figge", "Pacific Coast Motorsports", "68", "+ 1 Lap", "16", "5"], ["12", "Katherine Legge", "Dale Coyne Racing", "69", "+44.860", "14", "9"], ["5", "Neel Jani", "PKV Racing", "69", "+22.262", "4", "21"], ["17", "Paul Tracy", "Forsythe Racing", "14", "Mechanical", "17", "4"], ["7", "Sébastien Bourdais", "N/H/L Racing", "69", "+22.955", "1", "18"], ["10", "Ryan Dalziel", "Pacific Coast Motorsports", "69", "+29.554", "15", "11"], ["8", "Oriol Servià", "Forsythe Racing", "69", "+23.406", "13", "15"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who placed last? | Paul Tracy | 128 | Answer: |
Table InputTable: [["Rank", "Player Name", "No. of Titles", "Runner-up", "Final Appearances"], ["9", "Thierry Lincou", "1", "1", "2"], ["12", "Rodney Martin", "1", "0", "1"], ["1", "Jansher Khan", "8", "1", "9"], ["17", "James Willstrop", "0", "1", "1"], ["3", "Geoff Hunt", "4", "1", "5"], ["8", "Peter Nicol", "1", "2", "3"], ["9", "Rodney Eyles", "1", "1", "2"], ["17", "Lee Beachill", "0", "1", "1"], ["17", "Mohamed El Shorbagy", "0", "1", "1"], ["17", "Ahmed Barada", "0", "1", "1"], ["17", "Peter Marshall", "0", "1", "1"], ["14", "Chris Dittmar", "0", "5", "5"], ["2", "Jahangir Khan", "6", "3", "9"], ["15", "Qamar Zaman", "0", "4", "4"], ["6", "Ramy Ashour", "2", "1", "3"], ["6", "David Palmer", "2", "1", "3"], ["4", "Amr Shabana", "4", "0", "4"], ["17", "Dean Williams", "0", "1", "1"], ["17", "Karim Darwish", "0", "1", "1"], ["15", "Grégory Gaultier", "0", "4", "4"], ["9", "Ross Norman", "1", "1", "2"], ["17", "John White", "0", "1", "1"], ["17", "Mohibullah Khan", "0", "1", "1"], ["5", "Nick Matthew", "3", "0", "3"], ["12", "Jonathon Power", "1", "0", "1"], ["17", "Del Harris", "0", "1", "1"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which player has the most titles? | Jansher Khan | 128 | Answer: |
Table InputTable: [["Rank", "Pair", "Country", "Athletes", "Time", "Deficit"], ["5", "1", "Norway", "Sverre Lunde Pedersen\\nHåvard Bøkko\\nKristian Reistad Fredriksen", "3:46.33", "+4.90"], ["6", "3", "Germany", "Patrick Beckert\\nMarco Weber\\nRobert Lehmann", "3:46.48", "+5.05"], ["", "3", "Netherlands", "Sven Kramer\\nKoen Verweij\\nJan Blokhuijsen", "3:41.43", ""], ["", "2", "Russia", "Ivan Skobrev\\nDenis Yuskov\\nYevgeny Lalenkov", "3:43.62", "+2.19"], ["8", "2", "Poland", "Zbigniew Bródka\\nKonrad Niedźwiedzki\\nJan Szymański", "3:47.72", "+6.29"], ["7", "4", "South Korea", "Lee Seung-hoon\\nJoo Hyong-jun\\nKo Byung-wook", "3:47.18", "+5.75"], ["", "4", "United States", "Shani Davis\\nBrian Hansen\\nJonathan Kuck", "3:43.42", "+1.99"], ["4", "1", "Canada", "Denny Morrison\\nMathieu Giroux\\nLucas Makowsky", "3:44.38", "+2.95"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:norway finished 5th. who was the previous team that finished? | Canada | 128 | Answer: |
Table InputTable: [["Date", "Venue", "Opponent", "Result", "Scoreline", "China scorers"], ["Dec 17", "Muscat", "Oman", "Lost", "1-3", "Qu Bo 58'"], ["Dec 19", "Muscat", "Iran", "Lost", "0-2", ""], ["March 15", "Kunming", "Thailand", "Drawn", "3-3", "Qu Bo 34'\\nHan Peng 67'\\nZhu Ting 90'"], ["April 23", "Los Angeles", "El Salvador", "Drawn", "2-2", "Xiao Zhanbo 62' pen\\nQu Bo 63'"], ["January 20", "Zhongshan", "Lebanon", "Drawn", "0-0", "—"], ["January 10", "Dubai", "United Arab Emirates", "Drawn", "0-0", "—"], ["January 27", "Zhongshan", "Syria", "Won", "2-1", "Qu Bo 64'\\nZhu Ting 90'"], ["April 16", "Seattle", "Mexico", "Lost", "0-1", "—"], ["Dec 21", "Amman", "Jordan", "Won", "1-0", "Cao Yang 77'"], ["May 25", "Kunshan", "Jordan", "Won", "2-0", "Hao Junmin 23' pen\\nLi Weifeng 48'"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many dates are there? | 10 | 128 | Answer: |
Table InputTable: [["Pos", "Rider", "Manufacturer", "Time/Retired", "Points"], ["12", "Sebastian Porto", "Yamaha", "+27.054", "4"], ["11", "Alex Hofmann", "TSR-Honda", "+26.933", "5"], ["Ret", "Roberto Rolfo", "Aprilia", "Retirement", ""], ["23", "Arno Visscher", "Aprilia", "+1:40.635", ""], ["2", "Valentino Rossi", "Aprilia", "+0.180", "20"], ["7", "Franco Battaini", "Aprilia", "+20.889", "9"], ["22", "Rudie Markink", "Aprilia", "+1:40.280", ""], ["Ret", "Marcellino Lucchi", "Aprilia", "Retirement", ""], ["24", "Henk Van De Lagemaat", "Honda", "+1 Lap", ""], ["8", "Stefano Perugini", "Honda", "+20.891", "8"], ["6", "Ralf Waldmann", "Aprilia", "+7.019", "10"], ["Ret", "Andre Romein", "Honda", "Retirement", ""], ["1", "Loris Capirossi", "Honda", "38:04.730", "25"], ["20", "Lucas Oliver Bulto", "Yamaha", "+1:25.758", ""], ["Ret", "Maurice Bolwerk", "TSR-Honda", "Retirement", ""], ["16", "Luca Boscoscuro", "TSR-Honda", "+56.432", ""], ["17", "Johann Stigefelt", "Yamaha", "+1:07.433", ""], ["19", "Fonsi Nieto", "Yamaha", "+1:25.622", ""], ["3", "Jeremy McWilliams", "Aprilia", "+0.534", "16"], ["18", "Julien Allemand", "TSR-Honda", "+1:16.347", ""], ["13", "Tomomi Manako", "Yamaha", "+27.903", "3"], ["15", "Jarno Janssen", "TSR-Honda", "+56.248", "1"], ["21", "David Garcia", "Yamaha", "+1:33.867", ""], ["4", "Tohru Ukawa", "Honda", "+0.537", "13"], ["14", "Masaki Tokudome", "TSR-Honda", "+33.161", "2"], ["5", "Shinya Nakano", "Yamaha", "+0.742", "11"], ["10", "Anthony West", "TSR-Honda", "+26.816", "6"], ["9", "Jason Vincent", "Honda", "+21.310", "7"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who finished sooner, hofmann or porto? | Alex Hofmann | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["2006", "Lusophony Games", "Macau", "1st", "800 m", "2:07.34"], ["2011", "All-Africa Games", "Maputo, Mozambique", "12th (h)", "800 m", "2:06.72"], ["2009", "Lusophony Games", "Lisbon, Portugal", "4th", "800 m", "2:07.48"], ["2007", "All-Africa Games", "Algiers, Algeria", "1st", "800 m", "2:02.83"], ["2003", "All-Africa Games", "Abuja, Nigeria", "11th (h)", "800 m", "2:05.19"], ["2009", "World Championships", "Berlin, Germany", "36th (h)", "800 m", "2:06.72"], ["2006", "Commonwealth Games", "Melbourne, Australia", "9th (sf)", "800 m", "2:01.84"], ["2006", "African Championships", "Bambous, Mauritius", "13th (h)", "800 m", "2:10.50"], ["2008", "African Championships", "Addis Ababa, Ethiopia", "6th", "800 m", "2:05.95"], ["2010", "Commonwealth Games", "Delhi, India", "–", "800 m", "DNF"], ["2010", "African Championships", "Nairobi, Kenya", "7th", "800 m", "2:08.45"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the first year? | 2003 | 128 | Answer: |
Table InputTable: [["Represents", "Contestant", "Age", "Height", "Hometown"], ["Darién", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Panamá Centro", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Panamá Oeste", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Chiriquí Occidente", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Herrera", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Panamá Este", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Chiriquí", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Bocas del Toro", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Comarcas", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Veraguas", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Los Santos", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Colón", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Coclé", "TBD", "TBD", "0.0 m (0 in)", "TBD"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what location is listed before darien? | Colón | 128 | Answer: |
Table InputTable: [["No. in\\nseries", "No. in\\nseason", "Title", "Directed by", "Written by", "Original air date", "Prod.\\ncode"], ["26", "25", "\"My Mother, The Spy\"", "Richard Benedict", "Howard Gast", "March 30, 1966", "125"], ["3", "1", "\"So Long, Patrick Henry\"", "Leo Penn", "Robert Culp", "September 15, 1965", "101"], ["28", "27", "\"It's All Done with Mirrors\"", "Robert Butler", "Stephen Kandell", "April 13, 1966", "127"], ["23", "23", "\"A Day Called 4 Jaguar\"", "Richard Sarafian", "Michael Zagar", "March 9, 1966", "123"], ["4", "7", "\"Danny was a Million Laughs\"", "Mark Rydell", "Arthur Dales", "October 27, 1965", "107"], ["2", "3", "\"Carry Me Back to Old Tsing-Tao\"", "Mark Rydell", "David Karp", "September 29, 1965", "103"], ["27", "26", "\"There was a Little Girl\"", "John Rich", "Teleplay by: Stephen Kandell Story by: Robert Bloch", "April 6, 1966", "126"], ["11", "10", "\"Tatia\"", "David Friedkin", "Robert Lewin", "November 17, 1965", "110"], ["1", "14", "\"Affair in T'Sien Cha\"", "Sheldon Leonard", "Morton Fine & David Friedkin", "December 29, 1965", "114"], ["10", "8", "\"The Time of the Knife\"", "Paul Wendkos", "Gilbert Ralston", "November 3, 1965", "108"], ["24", "28", "\"One Thousand Fine\"", "Paul Wendkos", "Eric Bercovici", "April 27, 1966", "128"], ["14", "12", "\"Three Hours on a Sunday\"", "Paul Wendkos", "Morton Fine & David Friedkin", "December 8, 1965", "112"], ["25", "24", "\"Crusade to Limbo\"", "Richard Sarafian", "Teleplay by: Morton Fine & David Freidkin & Jack Turley Story by: Jack Turley", "March 23, 1966", "124"], ["16", "15", "\"The Tiger\"", "Paul Wendkos", "Robert Culp", "January 5, 1966", "115"], ["7", "5", "\"Dragon's Teeth\"", "Leo Penn", "Gilbert Ralston", "October 13, 1965", "105"], ["8", "6", "\"The Loser\"", "Mark Rydell", "Robert Culp", "October 20, 1965", "106"], ["21", "21", "\"Return to Glory\"", "Robert Sarafian", "David Friedkin & Morton Fine", "February 23, 1966", "121"], ["18", "18", "\"Court of the Lion\"", "Robert Culp", "Robert Culp", "February 2, 1966", "118"], ["22", "22", "\"The Conquest of Maude Murdock\"", "Paul Wendkos", "Robert C. Dennis & Earl Barrett", "March 2, 1966", "122"], ["5", "2", "\"A Cup of Kindness\"", "Leo Penn", "Morton Fine & David Friedkin", "September 22, 1965", "102"], ["19", "19", "\"Turkish Delight\"", "Paul Wendkos", "Eric Bercovici", "February 9, 1966", "119"], ["12", "11", "\"Weight of the World\"", "Paul Wendkos", "Robert Lewin", "December 1, 1965", "111"], ["15", "17", "\"Always Say Goodbye\"", "Allen Reisner", "Robert C. Dennis & Earl Barrett", "January 26, 1966", "117"], ["13", "13", "\"Tigers of Heaven\"", "Allen Reisner", "Morton Fine & David Friedkin", "December 15, 1965", "113"], ["20", "20", "\"Bet Me a Dollar\"", "Richard Sarafian", "David Friedkin & Morton Fine", "February 16, 1966", "120"], ["17", "16", "\"The Barter\"", "Allen Reisner", "Harvey Bullock & P.S. Allen", "January 12, 1966", "116"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many writers wrote at least 3 "i spy" episodes? | 3 | 128 | Answer: |
Table InputTable: [["Band", "Disc/Song", "Released", "Disc Description", "Disk Size", "Image"], ["Guns N' Roses", "Nightrain", "1989", "Shape of a suitcase", "7\"", ""], ["Guns N' Roses", "Paradise City", "1989", "Shape of a Colt \"Peacemaker\"", "7\"", ""], ["Guns N' Roses", "Sweet Child o' Mine", "1988", "Shape of the classic logo of the cross and skulls of the five band members", "7\"", ""], ["The Enemy", "You're not alone", "2007", "Square shaped. Has the single cover art on the A-side and a black and white picture of the band on the B-side with track listing.", "7\"", ""], ["Barnes & Barnes", "Fish Heads: Barnes & Barnes' Greatest Hits", "1982", "Shaped as a fish head", "12\"", ""], ["Monster Magnet", "Dopes to Infinity", "1995", "Shaped like the lead singer Dave Wyndorf's head.", "12\"", ""], ["The Coconuts (Side project of Kid Creole and the Coconuts)", "Did You Have To Love Me Like You Did", "1983", "In the shape of a coconut.", "7\"", ""], ["U2", "The Unforgettable Fire (single)", "1985", "Shaped as letter & number \"U2\" with various pictures of the band from the period.", "7\"", "U2"], ["Monster Magnet", "Negasonic Teenage Warhead", "", "Shaped like a mushroom cloud", "12\"", ""], ["Red Box", "Lean On Me b/w Stinging Bee", "1985", "Hexagonal red vinyl. Looks like a red box in 2D; flipside is a band photo.", "7\"", ""], ["Devo", "Beautiful World b/w Nu-Tra", "1981", "Shaped like an astronaut head", "", ""], ["Joe Strummer", "Love Kills", "", "Shaped like a gun", "7\"", "A gun"], ["Kiss", "Lick It Up", "1983", "Shaped like an armored tank", "", ""], ["Yeah Yeah Yeahs", "Cheated Hearts", "2006", "Heart shaped.", "7\"", ""], ["Gary Numan", "Warriors", "1983", "Shaped like a Jet Fighter.", "7\"", ""], ["The Fat Boys", "Wipe Out", "", "Shaped like a Hamburger", "7\"", ""], ["Men Without Hats", "I Got the Message", "1983", "", "", ""], ["Gary Numan", "Berserker", "1984", "Shaped like Numan's head.", "7\"", ""], ["Killing Joke", "Loose Cannon", "2003", "shaped yellow evil clown head image from the eponymous 2003 album sleeve", "", ""], ["OMD", "La Femme Accident", "1985", "", "", ""], ["Saxon", "Back on the Streets Again", "", "Shaped as an apple (as is printed on one side of the disk).", "7\"", ""], ["Broken English", "Comin on Strong", "1987", "Shaped as the 3 band members wearing Ghostbusters outfits holding guitars.", "", ""], ["The Mars Volta", "Mr. Muggs", "2008", "In the shape of a clear planchette.", "7\"", ""], ["Gangrene", "Sawblade EP", "2010", "In the shape of a circular sawblade.", "", ""], ["Less Than Jake", "Cheese", "1998", "Shaped like a piece of swiss cheese. 1000 pressed in yellow. 500 pressed in green (\"Moldy Version\").", "7\"", ""], ["Tangerine Dream", "Warsaw in the Sun", "1984", "The record is in the shape of Poland and has several images including Lech Wałęsa and Pope John Paul II.", "7\"", ""], ["Men Without Hats", "The Safety Dance", "1982", "Oddly shaped picture disc of a man and a woman dancing", "", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how long was the time between the release of guns n' roses songs "sweet child o' mine" and "nightrain"? | 1 year | 128 | Answer: |
Table InputTable: [["Stage", "Date", "Route", "Terrain", "Length", "Winner", "Race leader"], ["2", "1 July", "Caen – Dinan", "Plain stage", "212 km (132 mi)", "Max Bulla (AUT)", "Max Bulla (AUT)"], ["9", "8 July", "Pau – Luchon", "Stage with mountain(s)", "231 km (144 mi)", "Antonin Magne (FRA)", "Antonin Magne (FRA)"], ["8", "7 July", "Bayonne – Pau", "Plain stage", "106 km (66 mi)", "Charles Pélissier (FRA)", "Charles Pélissier (FRA)"], ["5", "4 July", "Vannes – Les Sables d'Olonne", "Plain stage", "202 km (126 mi)", "Charles Pélissier (FRA)", "Charles Pélissier (FRA)\\n Rafaele di Paco (ITA)"], ["16", "18 July", "Gap – Grenoble", "Stage with mountain(s)", "102 km (63 mi)", "Charles Pélissier (FRA)", "Antonin Magne (FRA)"], ["3", "2 July", "Dinan – Brest", "Plain stage", "206 km (128 mi)", "Fabio Battesini (ITA)", "Léon Le Calvez (FRA)"], ["21", "23 July", "Colmar – Metz", "Plain stage", "192 km (119 mi)", "Rafaele di Paco (ITA)", "Antonin Magne (FRA)"], ["12", "13 July", "Montpellier – Marseille", "Plain stage", "207 km (129 mi)", "Max Bulla (AUT)", "Antonin Magne (FRA)"], ["1", "30 June", "Paris – Caen", "Plain stage", "208 km (129 mi)", "Alfred Haemerlinck (BEL)", "Alfred Haemerlinck (BEL)"], ["11", "12 July", "Perpignan – Montpellier", "Plain stage", "164 km (102 mi)", "Rafaele di Paco (ITA)", "Antonin Magne (FRA)"], ["10", "10 July", "Luchon – Perpignan", "Stage with mountain(s)", "322 km (200 mi)", "Rafaele di Paco (ITA)", "Antonin Magne (FRA)"], ["15", "17 July", "Nice – Gap", "Stage with mountain(s)", "233 km (145 mi)", "Jef Demuysere (BEL)", "Antonin Magne (FRA)"], ["17", "19 July", "Grenoble – Aix-les-Bains", "Stage with mountain(s)", "230 km (140 mi)", "Max Bulla (AUT)", "Antonin Magne (FRA)"], ["20", "22 July", "Belfort – Colmar", "Stage with mountain(s)", "209 km (130 mi)", "André Leducq (FRA)", "Antonin Magne (FRA)"], ["22", "24 July", "Metz – Charleville", "Plain stage", "159 km (99 mi)", "Raffaele di Paco (ITA)", "Antonin Magne (FRA)"], ["7", "6 July", "Bordeaux – Bayonne", "Plain stage", "180 km (110 mi)", "Gérard Loncke (BEL)", "Rafaele di Paco (ITA)"], ["19", "21 July", "Evian – Belfort", "Stage with mountain(s)", "282 km (175 mi)", "Rafaele di Paco (ITA)", "Antonin Magne (FRA)"], ["23", "25 July", "Charleville – Malo-les-Bains", "Plain stage", "271 km (168 mi)", "Gaston Rebry (BEL)", "Antonin Magne (FRA)"], ["4", "3 July", "Brest – Vannes", "Plain stage", "211 km (131 mi)", "André Godinat (FRA)", "Rafaele di Paco (ITA)"], ["18", "20 July", "Aix-les-Bains – Evian", "Stage with mountain(s)", "204 km (127 mi)", "Jef Demuysere (BEL)", "Antonin Magne (FRA)"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how long is the caen-dinan route? (in miles) | 132 mi | 128 | Answer: |
Table InputTable: [["City", "County", "Population (2011)", "Population (2002)", "Altitude (m)", "Year status\\ngranted* or\\nfirst attested†"], ["Baia Mare", "Maramureș", "123,738", "137,976", "225", "1329†"], ["Baia Sprie", "Maramureș", "15,476", "15,735", "", ""], ["Bucharest", "-", "1,883,425", "1,926,334", "85", "1459†"], ["Toplița", "Harghita", "13,929", "16,839", "", "1956*"], ["Șomcuta Mare", "Maramureș", "7,565", "7,708", "", "2004*"], ["Boldești-Scăeni", "Prahova", "11,137", "11,505", "", ""], ["Găești", "Dâmbovița", "13,317", "16,598", "", ""], ["Ungheni", "Mureș", "6,945", "6,554", "", "2004*"], ["Zalău", "Sălaj", "56,202", "63,305", "", "1473*"], ["Vașcău", "Bihor", "2,315", "3,032", "", "1956*"], ["Urlați", "Prahova", "10,541", "11,858", "", ""], ["Orșova", "Mehedinți", "10,441", "15,379", "", ""], ["Beiuș", "Bihor", "10,667", "12,089", "", "1451*"], ["Geoagiu", "Hunedoara", "5,294", "6,290", "", "2000*"], ["Abrud", "Alba", "5,072", "6,803", "", ""], ["Ploiești", "Prahova", "209,945", "232,527", "150", "1596†"], ["Carei", "Satu Mare", "21,112", "25,590", "", "1871*"], ["Breaza", "Prahova", "15,928", "18,863", "", "1952*"], ["Hârșova", "Constanța", "9,642", "11,198", "", ""], ["Jimbolia", "Timiș", "10,808", "10,497", "", "1950*"], ["Marghita", "Bihor", "15,770", "18,650", "", "1967*"], ["Budești", "Călărași", "7,725", "9,596", "", "1989*"], ["Sângeorz-Băi", "Bistrița-Năsăud", "9,679", "10,702", "", ""], ["Ulmeni", "Maramureș", "7,270", "7,153", "", "2004*"], ["Slănic Moldova", "Bacău", "4,198", "5,375", "", ""], ["Topoloveni", "Argeș", "10,219", "10,329", "", "1968*"], ["Comarnic", "Prahova", "11,970", "13,532", "", "1968*"], ["Moreni", "Dâmbovița", "18,687", "22,868", "", "1948*"], ["Cavnic", "Maramureș", "4,976", "5,494", "", "1968*"], ["Seini", "Maramureș", "8,987", "9,439", "", "1989*"], ["Oradea", "Bihor", "196,367", "206,614", "150", "1113†"], ["Târgu Frumos", "Iași", "10,475", "13,763", "", ""], ["Hârlău", "Iași", "10,905", "12,260", "", "1968*"], ["Călărași", "Călărași", "65,181", "70,039", "13", "1534†"], ["Buhuși", "Bacău", "14,562", "21,993", "", "1930*"], ["Predeal", "Brașov", "4,755", "5,615", "1030", "1935*"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:city with the highest altitude with a population greater than 100,000 | Brașov | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Notes"], ["1999", "World Youth Championships", "Bydgoszcz, Poland", "12th", ""], ["2002", "World Junior Championships", "Kingston, Jamaica", "2nd", "63.91 m"], ["2009", "World Championships", "Berlin, Germany", "3rd", "74.79 m"], ["2001", "World Championships", "Edmonton, Canada", "23rd", "61.26 m"], ["2000", "World Junior Championships", "Santiago, Chile", "5th", ""], ["2001", "European Junior Championships", "Grosseto, Italy", "2nd", "61.97 m"], ["2007", "World Championships", "Osaka, Japan", "13th", "68.15 m"], ["2009", "World Athletics Final", "Thessaloniki, Greece", "3rd", "70.45 m"], ["2002", "European Championships", "Munich, Germany", "26th", "60.28 m"], ["2009", "World Student Games", "Belgrade, Serbia", "2nd", "72.85 m"], ["2008", "Olympic Games", "Beijing, PR China", "8th", "71.00 m"], ["2007", "World Student Games", "Bangkok, Thailand", "5th", "64.95 m"], ["2008", "World Athletics Final", "Stuttgart, Germany", "2nd", "71.40 m"], ["2012", "European Championships", "Helsinki, Finland", "2nd", "73.34 m"], ["2006", "European Championships", "Gothenburg, Sweden", "26th", "62.39 m"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what year did she receive her most recent highest rated position? | 2012 | 128 | Answer: |
Table InputTable: [["Pos", "Name", "Team", "Laps", "Time/Retired", "Grid", "Points"], ["12", "Katherine Legge", "Dale Coyne Racing", "69", "+44.860", "14", "9"], ["3", "Bruno Junqueira", "Dale Coyne Racing", "69", "+8.419", "11", "26"], ["1", "Justin Wilson", "RuSPORT", "69", "1:46:02.236", "2", "32"], ["9", "Graham Rahal", "N/H/L Racing", "69", "+23.949", "6", "13"], ["7", "Sébastien Bourdais", "N/H/L Racing", "69", "+22.955", "1", "18"], ["17", "Paul Tracy", "Forsythe Racing", "14", "Mechanical", "17", "4"], ["4", "Tristan Gommendy", "PKV Racing", "69", "+9.037", "3", "23"], ["10", "Ryan Dalziel", "Pacific Coast Motorsports", "69", "+29.554", "15", "11"], ["8", "Oriol Servià", "Forsythe Racing", "69", "+23.406", "13", "15"], ["2", "Jan Heylen", "Conquest Racing", "69", "+7.227", "7", "27"], ["16", "Alex Figge", "Pacific Coast Motorsports", "68", "+ 1 Lap", "16", "5"], ["5", "Neel Jani", "PKV Racing", "69", "+22.262", "4", "21"], ["6", "Simon Pagenaud", "Team Australia", "69", "+22.698", "5", "19"], ["11", "Dan Clarke", "Minardi Team USA", "69", "+38.903", "10", "11"], ["15", "Alex Tagliani", "Rocketsports", "68", "+ 1 Lap", "12", "6"], ["13", "Robert Doornbos", "Minardi Team USA", "69", "+1:00.638", "9", "8"], ["14", "Will Power", "Team Australia", "69", "+1:01.204", "8", "7"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many drivers are from the united kingdom? | 4 | 128 | Answer: |
Table InputTable: [["Place", "Player", "Country", "Score", "To par", "Money ($)"], ["T4", "Phil Mickelson", "United States", "68-70-72-74=284", "+4", "66,633"], ["1", "Corey Pavin", "United States", "72-69-71-68=280", "E", "350,000"], ["2", "Greg Norman", "Australia", "68-67-74-73=282", "+2", "207,000"], ["T10", "Frank Nobilo", "New Zealand", "72-72-70-71=285", "+5", "44,184"], ["T10", "Vijay Singh", "Fiji", "70-71-72-72=285", "+5", "44,184"], ["T10", "Bob Tway", "United States", "69-69-72-75=285", "+5", "44,184"], ["T4", "Jeff Maggert", "United States", "69-72-77-66=284", "+4", "66,633"], ["3", "Tom Lehman", "United States", "70-72-67-74=283", "+3", "131,974"], ["T4", "Bill Glasson", "United States", "69-70-76-69=284", "+4", "66,633"], ["T4", "Neal Lancaster", "United States", "70-72-77-65=284", "+4", "66,633"], ["T4", "Jay Haas", "United States", "70-73-72-69=284", "+4", "66,633"], ["T4", "Davis Love III", "United States", "72-68-73-71=284", "+4", "66,633"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the difference between the number of players from the us and the number of players from every other country? | 8 | 128 | Answer: |
Table InputTable: [["Hand", "Auto", "Wind", "Athlete", "Nationality", "Birthdate", "Location", "Date"], ["", "10.95", "", "George McNeill", "United Kingdom", "19.02.1947", "Melbourne", "31.11.1987"], ["", "10.90", "", "Thaddeus Bell", "United States", "28.11.1942", "Raleigh", "01.05.1988"], ["", "10.87", "", "Eddie Hart", "United States", "24.04.1949", "Eugene", "03.08.1989"], ["10.7", "", "", "Thane Baker", "United States", "04.10.1931", "Elkhart", "13.09.1972"], ["10.7", "", "", "Walt Butler", "United States", "21.03.1941", "Northridge", "16.05.1981"], ["", "10.60", "", "Bill Collins", "United States", "20.11.1950", "", "06.06.1992"], ["", "10.26", "2.3", "Troy Douglas", "Netherlands", "30.11.1962", "Utrecht", "10.07.2004"], ["", "10.84", "1.8", "Erik Oostweegel", "Netherlands", "29.04.1960", "Tilburg", "10.06.2000"], ["", "10.29", "1.9", "Troy Douglas", "Netherlands", "30.11.1962", "Leiden", "07.06.2003"], ["", "10.93", "0.6", "Gilles Echevin", "France", "01.09.1948", "Grenoble", "07.05.1989"], ["", "10.95", "", "Karl Heinz Schröder", "Germany", "17.06.1939", "Hannover", "28.07.1979"], ["10.7", "", "", "Klaus Jürgen Schneider", "Germany", "02.03.1942", "Stuttgart", "07.07.1982"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who's auto score is the same as george mcneill? | Karl Heinz Schröder | 128 | Answer: |
Table InputTable: [["Year", "Result", "Award", "Film"], ["2002", "Nominated", "ALMA Award Outstanding Actor in a Television Series", "Spin City"], ["2001", "Nominated", "ALMA Award Outstanding Actor in a Television Series", "Spin City"], ["2008", "Won", "ALMA Award Outstanding Actor in a Comedy Television Series", "Two and a Half Men"], ["2007", "Nominated", "Emmy Award for Outstanding Lead Actor - Comedy Series", "Two and a Half Men"], ["2008", "Nominated", "Outstanding Lead Actor - Comedy Series", "Two and a Half Men"], ["2006", "Nominated", "Emmy Award for Outstanding Lead Actor - Comedy Series", "Two and a Half Men"], ["2007", "Nominated", "Teen Choice Award Choice TV Actor: Comedy", "Two and a Half Men"], ["2002", "Nominated", "Kids' Choice Awards Favorite Television Actor", "Two and a Half Men"], ["2010", "Nominated", "SAG Award Outstanding Performance by a Male Actor in a Comedy Series", "Two and a Half Men"], ["2008", "Nominated", "Teen Choice Awards Choice TV Actor: Comedy", "Two and a Half Men"], ["2006", "Nominated", "Golden Globe Award for Best Actor – Television Series Musical or Comedy", "Two and a Half Men"], ["2005", "Nominated", "SAG Award Outstanding Performance by a Male Actor in a Comedy Series", "Two and a Half Men"], ["2005", "Nominated", "Golden Globe Award for Best Actor – Television Series Musical or Comedy", "Two and a Half Men"], ["2006", "Won", "Golden Icon Award Best Actor - Comedy Series", "Two and a Half Men"], ["2002", "Won", "Golden Globe Award Best Performance by an Actor in a Television Series - Musical or Comedy", "Spin City"], ["2007", "Nominated", "People's Choice Award Favorite Male TV Star", ""], ["2008", "Nominated", "People's Choice Award Favorite Male TV Star", ""], ["1999", "Nominated", "SAG Award Outstanding Performance by a Cast in a Theatrical Motion Picture", "Being John Malkovich"], ["1999", "Nominated", "Online Film Critics Society Award for Best Cast", "Being John Malkovich"], ["1989", "Won", "Bronze Wrangler Theatrical Motion Picture", "Young Guns"], ["2012", "Won", "WWE Slammy Award Top Social Media Ambassador", "WWE Raw"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which film had two consecutive nominations for the alma award outstanding actor in a television series? | Spin City | 128 | Answer: |
Table InputTable: [["Match Day", "Date", "Opponent", "H/A", "Score", "Aberdeen Scorer(s)", "Attendance"], ["28", "6 February", "Morton", "H", "2–0", "Brewster, Archibald", "2,000"], ["4", "5 September", "Clyde", "H", "2–0", "MacLachlan, Archibald", "6,000"], ["2", "22 August", "Rangers", "H", "0–2", "", "15,000"], ["34", "20 March", "Airdrieonians", "H", "3–0", "Brewster, Cail, Main", "5,500"], ["30", "20 February", "Hibernian", "H", "0–0", "", "8,500"], ["32", "6 March", "Partick Thistle", "H", "0–0", "", "6,000"], ["19", "12 December", "Partick Thistle", "A", "0–3", "", "6,000"], ["31", "27 February", "Third Lanark", "A", "1–0", "Walker", "5,000"], ["20", "19 December", "Kilmarnock", "H", "3–0", "MacLachlan, Cail, Main", "4,000"], ["5", "12 September", "Ayr United", "A", "0–1", "", "2,000"], ["23", "2 January", "Raith Rovers", "A", "1–5", "Cail", "6,000"], ["1", "15 August", "Dundee", "A", "3–1", "Soye, Walker, Cail", "10,000"], ["29", "13 February", "St. Mirren", "A", "2–0", "Cail, Walker", "3,000"], ["35", "27 March", "Rangers", "A", "1–1", "W. Wylie", "10,000"], ["14", "7 November", "Raith Rovers", "H", "1–3", "Main", "6,000"], ["24", "9 January", "Ayr United", "H", "1–1", "Cail", "4,500"], ["25", "16 January", "Clyde", "A", "0–3", "", "3,000"], ["22", "1 January", "Dundee", "H", "2–1", "Walker, J. Wyllie", "7,000"], ["18", "5 December", "Celtic", "H", "0–1", "", "7,000"], ["11", "17 October", "Third Lanark", "H", "1–2", "Archibald", "6,000"], ["3", "29 August", "Morton", "A", "1–1", "Cail", "4,500"], ["27", "30 January", "Dumbarton", "A", "2–3", "Cail, Walker", "3,000"], ["16", "21 November", "Dumbarton", "H", "0–0", "", "5,000"], ["13", "31 October", "Hibernian", "A", "2–1", "Chatwin, Main", "4,000"], ["6", "19 September", "Motherwell", "H", "3–1", "J. Wyllie, MacLachlan, Walker", "7,000"], ["12", "24 October", "Falkirk", "A", "1–1", "J. Wyllie", "5,500"], ["17", "28 November", "Kilmarnock", "A", "2–5", "MacLachlan, McLeod", "2,500"], ["26", "23 January", "Falkirk", "H", "1–2", "Walker", "4,000"], ["7", "26 September", "Heart of Midlothian", "A", "0–2", "", "14,000"], ["37", "10 April", "Celtic", "A", "0–1", "", "10,000"], ["21", "26 December", "Motherwell", "A", "1–1", "Walker", "3,000"], ["38", "17 April", "Hamilton Academical", "H", "1–0", "J. Wyllie", "4,000"], ["10", "10 October", "Airdrieonians", "A", "0–3", "", "7,000"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many games did archibald score a point this season? | 3 | 128 | Answer: |
Table InputTable: [["Sl no", "Name of the prabandham", "Starting from", "Ending with", "Number of pasurams", "Sung by"], ["", "Total number of pasurams", "", "", "3776", ""], ["23", "Thiruvay Mozhi", "2674", "3776", "1102", "Nammalvar"], ["20", "Thiruvezhukkurrirukkai", "2672", "2672", "1", "Thirumangai alvar"], ["15", "Moonram Thiruvandhadhi", "2282", "2381", "100", "Peyalvar"], ["11", "Kurun Thandagam", "2032", "2051", "20", "Thirumangai alvar"], ["12", "Nedum Thandagam", "2052", "2081", "30", "Thirumangai alvar"], ["18", "Thiruvasiriyam", "2578", "2584", "7", "Nammalvar"], ["21", "Siriya Thirumadal", "2673", "2673", "1", "Thirumangai alvar"], ["13", "Mudhal Thiruvandhadhi", "2082", "2181", "100", "Poigai Alvar"], ["4", "Perumal Thirumozhi", "647", "751", "105", "Kulasekara alvar"], ["14", "Irandam Thiruvandhadhi", "2182", "2281", "100", "Bhoothathalvar"], ["19", "Peria Thiruvandhadhi", "2585", "2671", "87", "Nammalvar"], ["22", "Peria Thirumadal", "2674", "2674", "1", "Thirumangai alvar"], ["17", "Thiruviruththam", "2478", "2577", "100", "Nammalvar"], ["1", "Periazhvar Thirumozhi", "1", "473", "473", "Periyalvar"], ["10", "Peria Thirumozhi", "948", "2031", "1084", "Thirumangai alvar"], ["16", "Naanmugan Thiruvandhadhi", "2382", "2477", "96", "Thirumalisai alvar"], ["6", "Thirumalai", "872", "916", "45", "Thondaradippodi alvar"], ["2", "Thiruppavai", "474", "503", "30", "Aandaal"], ["7", "Thiruppalliyezhuchchi", "917", "926", "10", "Thondaradippodi alvar"], ["8", "Amalanadhi piran", "927", "936", "10", "Thiruppaan alvar"], ["5", "Thiruchchanda Viruththam", "752", "871", "120", "Thirumalisai alvar"], ["3", "Nachiar Tirumozhi", "504", "646", "143", "Aandaal"], ["9", "Kanni Nun Siruththambu", "937", "947", "11", "Madhurakavi Alvar"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:thiruvay mozhi has the most pasurams at what number total? | 1102 | 128 | Answer: |
Table InputTable: [["Date", "Time", "Opponent#", "Rank#", "Site", "TV", "Result", "Attendance"], ["September 8", "3:00 PM", "at Oregon State*", "#13", "Reser Stadium • Corvallis, OR", "FX", "L 7–10", "42,189"], ["September 15", "7:00 PM", "Utah State*", "#22", "Camp Randall Stadium • Madison, WI", "BTN", "W 16–14", "79,332"], ["November 24", "2:30 PM", "at Penn State", "", "Beaver Stadium • University Park, PA", "ESPN2", "L 21–24 OT", "93,505"], ["December 1", "7:00 PM", "vs. #14 Nebraska", "", "Lucas Oil Stadium • Indianapolis, IN (Big Ten Championship Game)", "FOX", "W 70–31", "41,260"], ["January 1, 2013", "4:10 PM", "vs. #8 Stanford", "#23", "Rose Bowl • Pasadena, CA (Rose Bowl)", "ESPN", "L 14–20", "93,259"], ["November 17", "2:30 PM", "Ohio State", "", "Camp Randall Stadium • Madison, WI", "ABC/ESPN2", "L 14–21 OT", "80,112"], ["October 13", "11:00 AM", "at Purdue", "", "Ross-Ade Stadium • West Lafayette, IN", "BTN", "W 38–14", "46,007"], ["September 29", "7:00 PM", "at #20 Nebraska", "#23", "Memorial Stadium • Lincoln, NE", "ABC", "L 27–30", "85,962"], ["October 27", "2:30 PM", "Michigan State", "#25", "Camp Randall Stadium • Madison, WI", "ABC/ESPN2", "L 13–16 OT", "80,538"], ["September 1", "2:30 PM", "#9 (FCS) Northern Iowa*", "#12", "Camp Randall Stadium • Madison, WI", "BTN", "W 26–21", "79,568"], ["November 10", "11:00 AM", "at Indiana", "", "Memorial Stadium • Bloomington, IN", "ESPN2", "W 62–14", "43,240"], ["September 22", "11:00 AM", "UTEP*", "#24", "Camp Randall Stadium • Madison, WI", "ESPN2", "W 37–26", "79,806"], ["October 6", "2:30 PM", "Illinois", "", "Camp Randall Stadium • Madison, WI", "ABC/ESPN2", "W 31–14", "80,096"], ["October 20", "11:00 AM", "Minnesota", "", "Camp Randall Stadium • Madison, WI (Paul Bunyan's Axe)", "ESPNU", "W 38–13", "80,587"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who do they play before oregon state? | #9 (FCS) Northern Iowa* | 128 | Answer: |
Table InputTable: [["Name", "Pennant", "Namesake", "Builder", "Ordered", "Laid down", "Launched", "Commissioned", "Fate"], ["Achilles", "70", "Achilles", "Cammell Laird", "16 February 1931", "11 June 1931", "1 September 1932", "24 March 1936", "Transferred to Royal New Zealand Navy as HMNZS Achilles 1941-1946\\nSold to Indian Navy as HIMS Delhi 1948"], ["Leander", "75", "Leander of Abydos", "HM Dockyard, Devonport", "18 February 1928", "1 August 1928", "13 July 1929", "23 July 1931", "Transferred to Royal New Zealand Navy as HMNZS Leander 1941-1945\\nBroken up at Blyth 1950"], ["Amphion", "29", "Amphion of Thebes", "HM Dockyard, Portsmouth", "1 December 1932", "22 June 1933", "27 July 1934", "15 June 1936", "Sold to Royal Australian Navy as HMAS Perth, 1939\\nSunk in torpedo attack, 1 March 1942"], ["Apollo", "63", "Apollo, God of Light", "HM Dockyard, Devonport", "1 March 1933", "15 August 1933", "9 October 1934", "13 January 1936", "Sold to Royal Australian Navy as HMAS Hobart, 1938\\nBroken up at Osaka, 1962"], ["Sydney\\n(ex-Phaeton)", "48", "City of Sydney", "Swan Hunter", "10 February 1933", "8 July 1933", "22 September 1934", "24 September 1935", "Sunk in surface action, 19 November 1941"], ["Ajax", "22", "Ajax the Great", "Vickers Armstrong", "1 October 1932", "7 February 1933", "1 March 1934", "12 April 1935", "Broken up at Newport, 1949"], ["Orion", "85", "Orion the Hunter", "HM Dockyard, Devonport", "24 March 1931", "26 September 1931", "24 November 1932", "18 January 1934", "Broken up at Dalmuir, 1949"], ["Neptune", "20", "Neptune, God of the Sea", "HM Dockyard, Portsmouth", "2 March 1931", "24 September 1931", "31 January 1933", "23 February 1934", "Sunk in minefield off Tripoli, 19 December 1941"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the total number of builder? | 5 | 128 | Answer: |
Table InputTable: [["Thread\\nnominal size", "Outer diameter\\n[mm (in)]", "Threads per inch\\n(TPI)", "Pitch\\n[in (mm)]", "Inner diameter\\n[mm (in)]", "Cable diameter\\n[mm (in)]"], ["PG42", "54.0 (2.126)", "16", "0.0625 (1.5875)", "52.48 (2.066)", ""], ["PG7", "12.5 (0.492)", "20", "0.05 (1.270)", "11.28 (0.444)", "3 to 6.5 (0.118 to 0.256)"], ["PG48", "59.3 (2.335)", "16", "0.0625 (1.5875)", "57.78 (2.275)", ""], ["PG29", "37.0 (1.457)", "16", "0.0625 (1.5875)", "35.48 (1.397)", "18 to 25 (0.709 to 0.984)"], ["PG16", "22.5 (0.886)", "18", "0.05556 (1.4112)", "21.16 (0.833)", "10 to 14 (0.394 to 0.551)"], ["PG36", "47.0 (1.850)", "16", "0.0625 (1.5875)", "45.48 (1.791)", ""], ["PG11", "18.6 (0.732)", "18", "0.05556 (1.4112)", "17.26 (0.680)", "5 to 10 (0.197 to 0.394)"], ["PG21", "28.3 (1.114)", "16", "0.0625 (1.5875)", "26.78 (1.054)", "13 to 18 (0.512 to 0.709)"], ["PG9", "15.5 (0.610)", "18", "0.05556 (1.4112)", "13.86 (0.546)", "4 to 8 (0.157 to 0.315)"], ["PG13.5", "20.4 (0.803)", "18", "0.05556 (1.4112)", "19.06 (0.750)", "6 to 12 (0.236 to 0.472)"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:does a pg7 screw have a larger pitch than a pg16 screw? | no | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["2007", "Universiade", "Bangkok, Thailand", "1st", "400 m", ""], ["2001", "World Youth Championships", "Debrecen, Hungary", "4th", "400 m", ""], ["2006", "World Cup", "Athens, Greece", "7th", "400 m", ""], ["2006", "Asian Games", "Doha, Qatar", "1st", "400 m", ""], ["2011", "Universiade", "Shenzhen, China", "–", "400 m", "DQ"], ["2006", "Asian Games", "Doha, Qatar", "2nd", "4x400 m relay", ""], ["2005", "Asian Championships", "Incheon, South Korea", "2nd", "4x400 m relay", ""], ["2005", "Universiade", "Izmir, Turkey", "6th", "4x400 m relay", ""], ["2002", "Asian Games", "Busan, South Korea", "2nd", "4x400 m relay", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:number of consecutive years she finished in top 10 | 3 | 128 | Answer: |
Table InputTable: [["Week", "Date", "Opponent", "Score", "Result", "Record"], ["9", "Oct 23", "at Hamilton Tiger-Cats", "25–17", "Loss", "1–10"], ["6", "Oct 2", "at Hamilton Tiger-Cats", "45–0", "Loss", "1–6"], ["5", "Sept 25", "vs. Hamilton Tiger-Cats", "38–12", "Loss", "1–5"], ["10", "Oct 30", "vs. Hamilton Tiger-Cats", "30–9", "Loss", "1–11"], ["11", "Nov 6", "at Toronto Argonauts", "18–12", "Loss", "1–12"], ["7", "Oct 11", "at Montreal Alouettes", "24–6", "Loss", "1–8"], ["1", "Aug 28", "at Toronto Argonauts", "13–6", "Loss", "0–1"], ["2", "Sept 4", "at Montreal Alouettes", "21–2", "Loss", "0–2"], ["4", "Sept 18", "vs. Toronto Argonauts", "34–6", "Loss", "1–4"], ["7", "Oct 9", "vs. Montreal Alouettes", "25–11", "Loss", "1–7"], ["8", "Oct 16", "vs. Toronto Argonauts", "27–11", "Loss", "1–9"], ["2", "Sept 6", "vs. Montreal Alouettes", "20–11", "Loss", "0–3"], ["3", "Sept 11", "at Toronto Argonauts", "12–5", "Win", "1–3"], ["12", "Nov 13", "vs. Montreal Alouettes", "14–12", "Win", "2–12"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the number of losses on the road? | 6 | 128 | Answer: |
Table InputTable: [["Round", "Round", "Race", "Date", "Pole Position", "Fastest Lap", "Winning Club", "Winning Team", "Report"], ["2", "R2", "Nürburgring", "September 21", "", "SC Corinthians", "PSV Eindhoven", "Azerti Motorsport", "Report"], ["4", "R2", "Estoril", "October 19", "", "Borussia Dortmund", "Al Ain", "Azerti Motorsport", "Report"], ["2", "R1", "Nürburgring", "September 21", "A.C. Milan", "PSV Eindhoven", "A.C. Milan", "Scuderia Playteam", "Report"], ["5", "R1", "Vallelunga", "November 2", "Liverpool F.C.", "Beijing Guoan", "Beijing Guoan", "Zakspeed", "Report"], ["6", "R2", "Jerez", "November 23", "", "Beijing Guoan", "Borussia Dortmund", "Zakspeed", "Report"], ["1", "R1", "Donington Park", "August 31", "Beijing Guoan", "Beijing Guoan", "Beijing Guoan", "Zakspeed", "Report"], ["1", "R2", "Donington Park", "August 31", "", "PSV Eindhoven", "Sevilla FC", "GTA Motor Competición", "Report"], ["3", "R2", "Zolder", "October 5", "", "Atlético Madrid", "Beijing Guoan", "Zakspeed", "Report"], ["3", "R1", "Zolder", "October 5", "Borussia Dortmund", "Liverpool F.C.", "Liverpool F.C.", "Hitech Junior Team", "Report"], ["6", "R1", "Jerez", "November 23", "Liverpool F.C.", "R.S.C. Anderlecht", "A.C. Milan", "Scuderia Playteam", "Report"], ["4", "R1", "Estoril", "October 19", "A.S. Roma", "Atlético Madrid", "Liverpool F.C.", "Hitech Junior Team", "Report"], ["5", "R2", "Vallelunga", "November 2", "", "Atlético Madrid", "F.C. Porto", "Hitech Junior Team", "Report"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which race came first? | Donington Park | 128 | Answer: |
Table InputTable: [["District", "Incumbent", "2008 Status", "Democratic", "Republican"], ["7", "Ed Perlmutter", "Re-election", "Ed Perlmutter", "John W. Lerew"], ["4", "Marilyn Musgrave", "Re-election", "Betsy Markey", "Marilyn Musgrave"], ["5", "Doug Lamborn", "Re-election", "Hal Bidlack", "Doug Lamborn"], ["3", "John Salazar", "Re-election", "John Salazar", "Wayne Wolf"], ["1", "Diana DeGette", "Re-election", "Diana DeGette", "George Lilly"], ["2", "Mark Udall", "Open", "Jared Polis", "Scott Starin"], ["6", "Tom Tancredo", "Open", "Hank Eng", "Mike Coffman"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many incumbents are on the table | 7 | 128 | Answer: |
Table InputTable: [["Date", "Date", "", "Rank", "Tournament name", "Venue", "City", "Winner", "Runner-up", "Score"], ["12–09", "12–15", "GER", "WR", "German Open", "NAAFI", "Osnabrück", "Ronnie O'Sullivan", "Alain Robidoux", "9–7"], ["05–??", "05–??", "WAL", "", "Pontins Professional", "Pontins", "Prestatyn", "Martin Clark", "Andy Hicks", "9–7"], ["10–29", "11–10", "THA", "", "World Cup", "Amari Watergate Hotel", "Bangkok", "Scotland", "Ireland", "10–7"], ["11–15", "12–01", "ENG", "WR", "UK Championship", "Guild Hall", "Preston", "Stephen Hendry", "John Higgins", "10–9"], ["03–27", "04–05", "ENG", "WR", "British Open", "Plymouth Pavilions", "Plymouth", "Mark Williams", "Stephen Hendry", "9–2"], ["02–23", "03–02", "MLT", "WR", "European Open", "Mediterranean Conference Centre", "Valletta", "John Higgins", "John Parrott", "9–5"], ["02–02", "02–09", "ENG", "", "Masters", "Wembley Conference Centre", "London", "Steve Davis", "Ronnie O'Sullivan", "10–8"], ["02–13", "02–22", "SCO", "WR", "International Open", "A.E.C.C.", "Aberdeen", "Stephen Hendry", "Tony Drago", "9–1"], ["12–28", "05–18", "ENG", "", "European League", "Diamond Centre", "Irthlingborough", "Ronnie O'Sullivan", "Stephen Hendry", "10–8"], ["01–02", "01–05", "ENG", "", "Charity Challenge", "International Convention Centre", "Birmingham", "Stephen Hendry", "Ronnie O'Sullivan", "9–8"], ["03–18", "03–23", "IRL", "", "Irish Masters", "Goff's", "Kill", "Stephen Hendry", "Darren Morgan", "9–8"], ["01–24", "02–01", "WAL", "WR", "Welsh Open", "Newport Leisure Centre", "Newport", "Stephen Hendry", "Mark King", "9–2"], ["10–16", "10–27", "ENG", "WR", "Grand Prix", "Bournemouth International Centre", "Bournemouth", "Mark Williams", "Euan Henderson", "9–5"], ["10–08", "10–13", "MLT", "", "Malta Grand Prix", "Jerma Palace Hotel", "Marsaskala", "Nigel Bond", "Tony Drago", "7–3"], ["04–19", "05–05", "ENG", "WR", "World Snooker Championship", "Crucible Theatre", "Sheffield", "Ken Doherty", "Stephen Hendry", "18–12"], ["09–09", "09–15", "THA", "WR", "Asian Classic", "Riverside Montien Hotel", "Bangkok", "Ronnie O'Sullivan", "Brian Morgan", "9–8"], ["09–24", "09–29", "SCO", "", "Scottish Masters", "Civic Centre", "Motherwell", "Peter Ebdon", "Alan McManus", "9–6"], ["03–10", "03–16", "THA", "WR", "Thailand Open", "Century Park Hotel", "Bangkok", "Peter Ebdon", "Nigel Bond", "9–7"], ["10–05", "10–14", "SCO", "", "Benson & Hedges Championship", "JP Snooker Centre", "Edinburgh", "Brian Morgan", "Drew Henry", "9–8"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the number of german flags on this table? | 1 | 128 | Answer: |
Table InputTable: [["Season", "Team", "Country", "Division", "Apps", "Goals"], ["2006", "Chengdu Wuniu", "China", "2", "1", "0"], ["2008", "Jiangsu Sainty", "China", "2", "24", "0"], ["2003", "Shandong Luneng", "China", "1", "4", "0"], ["2013", "Jiangsu Sainty", "China", "1", "11", "0"], ["2004", "Shandong Luneng", "China", "1", "0", "0"], ["2009", "Jiangsu Sainty", "China", "1", "15", "0"], ["2012", "Jiangsu Sainty", "China", "1", "0", "0"], ["2010", "Jiangsu Sainty", "China", "1", "17", "0"], ["2005", "Shandong Luneng", "China", "1", "0", "0"], ["2007", "Shandong Luneng", "China", "1", "0", "0"], ["2011", "Jiangsu Sainty", "China", "1", "9", "0"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many seasons did team chengdu wuniu have? | 1 | 128 | Answer: |
Table InputTable: [["Year", "Division", "League", "Regular Season", "Playoffs", "Open Cup"], ["2013", "4", "USL PDL", "4th, Heartland", "Did not qualify", "Did not qualify"], ["2007", "4", "USL PDL", "3rd, Heartland", "Did not qualify", "1st Round"], ["2008", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2012", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2009", "4", "USL PDL", "6th, Heartland", "Did not qualify", "Did not qualify"], ["2006", "4", "USL PDL", "3rd, Heartland", "Did not qualify", "Did not qualify"], ["2005", "4", "USL PDL", "3rd, Heartland", "Did not qualify", "Did not qualify"], ["2004", "4", "USL PDL", "6th, Heartland", "Did not qualify", "Did not qualify"], ["2003", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["1999", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2002", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2011", "4", "USL PDL", "4th, Heartland", "Did not qualify", "Did not qualify"], ["2010", "4", "USL PDL", "7th, Heartland", "Did not qualify", "Did not qualify"], ["2001", "4", "USL PDL", "5th, Rocky Mountain", "Did not qualify", "Did not qualify"], ["2000", "4", "USL PDL", "4th, Rocky Mountain", "Did not qualify", "Did not qualify"], ["1998", "4", "USISL PDSL", "4th, Central", "Division Finals", "1st Round"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:when was the last time the kansas city brass placed 4th in the regular season standings? | 2013 | 128 | Answer: |
Table InputTable: [["Rank", "Player", "County", "Tally", "Total", "Opposition"], ["6", "Gary Kirby", "Limerick", "0–9", "9", "Antrim"], ["3", "Gary Kirby", "Limerick", "0–10", "10", "Tipperary"], ["6", "Seán McLoughlin", "Westmeath", "1–6", "9", "Carlow"], ["6", "David Martin", "Meath", "1–6", "9", "Offaly"], ["3", "Gary Kirby", "Limerick", "1–7", "10", "Tipperary"], ["9", "Paul Flynn", "Waterford", "1–5", "8", "Tipperary"], ["9", "John Leahy", "Tipperary", "2–2", "8", "Kerry"], ["9", "John Byrne", "Carlow", "2–2", "8", "Westmeath"], ["3", "Kevin Broderick", "Galway", "3–1", "10", "New York"], ["2", "Niall English", "Carlow", "1–9", "12", "Westmeath"], ["9", "Tom Dempsey", "Wexford", "1–5", "8", "Offaly"], ["9", "John Troy", "Offaly", "2–2", "8", "Laois"], ["1", "Francis Forde", "Galway", "2–8", "14", "Roscommon"], ["9", "Francis Forde", "Galway", "1–5", "8", "New York"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who is the top ranking player? | Francis Forde | 128 | Answer: |
Table InputTable: [["Golfer", "Country", "Wins", "Match Play", "Championship", "Invitational", "Champions"], ["Ernie Els", "South Africa", "2", "—", "2: 2004, 2010", "—", "—"], ["Geoff Ogilvy", "Australia", "3", "2: 2006, 2009", "1: 2008", "—", "—"], ["Phil Mickelson", "United States", "2", "—", "1: 2009", "—", "1: 2009"], ["Ian Poulter", "England", "2", "1: 2010", "—", "—", "1: 2012"], ["Darren Clarke", "Northern Ireland", "2", "1: 2000", "—", "1: 2003", "—"], ["Hunter Mahan", "United States", "2", "1: 2012", "—", "1: 2010", "—"], ["Tiger Woods", "United States", "18", "3: 2003, 2004, 2008", "7: 1999, 2002, 2003,\\n2005, 2006, 2007, 2013", "8: 1999, 2000, 2001, 2005,\\n2006, 2007, 2009, 2013", "—"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:whats the difference in win amounts between geoff ogilvy and ernie els? | 1 | 128 | Answer: |
Table InputTable: [["Season", "Episodes", "Time slot (EST)", "Original airing\\nSeason premiere", "Original airing\\nSeason finale", "Original airing\\nTV season", "Rank", "Viewers\\n(in millions)"], ["1", "23", "Friday 9pm/8c (October 6, 2000 – January 12, 2001)\\nThursday 9pm/8c (February 1, 2001 – May 17, 2001)", "October 6, 2000", "May 17, 2001", "2000–2001", "#10", "17.80"], ["9", "24", "Thursday 9pm/8c", "October 9, 2008", "May 14, 2009", "2008–2009", "#4", "18.52"], ["5", "25", "Thursday 9pm/8c", "September 23, 2004", "May 19, 2005", "2004–2005", "#2", "26.26"], ["6", "24", "Thursday 9pm/8c", "September 22, 2005", "May 18, 2006", "2005–2006", "#3", "24.86"], ["4", "23", "Thursday 9pm/8c", "September 25, 2003", "May 20, 2004", "2003–2004", "#2", "25.27"], ["7", "24", "Thursday 9pm/8c", "September 21, 2006", "May 17, 2007", "2006–2007", "#4", "20.34"], ["8", "17", "Thursday 9pm/8c", "September 27, 2007", "May 15, 2008", "2007–2008", "#9", "16.62"], ["13", "22", "Wednesday 10pm/9c", "September 26, 2012", "May 15, 2013", "2012–2013", "#25", "11.63"], ["12", "22", "Wednesday 10pm/9c", "September 21, 2011", "May 9, 2012", "2011–2012", "#21", "12.49"], ["11", "22", "Thursday 9pm/8c", "September 23, 2010", "May 12, 2011", "2010–2011", "#12", "13.52"], ["3", "23", "Thursday 9pm/8c", "September 26, 2002", "May 15, 2003", "2002–2003", "#1", "26.20"], ["2", "23", "Thursday 9pm/8c", "September 27, 2001", "May 16, 2002", "2001–2002", "#2", "23.69"], ["10", "23", "Thursday 9pm/8c", "September 24, 2009", "May 20, 2010", "2009–2010", "#12", "14.92"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many episodes were in the last season on this chart? | 22 | 128 | Answer: |
Table InputTable: [["Date", "Opponent#", "Rank#", "Site", "TV", "Result", "Attendance"], ["October 22", "Ole Miss", "#8", "Bryant–Denny Stadium • Tuscaloosa, AL (Rivalry)", "ABC", "W 21–10", "70,123"], ["October 8", "Southern Miss*", "#11", "Bryant–Denny Stadium • Tuscaloosa, AL", "", "W 14–6", "70,123"], ["October 1", "Georgia", "#11", "Bryant–Denny Stadium • Tuscaloosa, AL", "ESPN", "W 29–28", "70,123"], ["September 10", "Vanderbilt", "#11", "Bryant–Denny Stadium • Tuscaloosa, AL", "JPS", "W 17–7", "70,123"], ["December 3", "vs. #6 Florida", "#3", "Georgia Dome • Atlanta, GA (SEC Championship Game)", "ABC", "L 23–24", "74,751"], ["November 5", "at LSU", "#6", "Tiger Stadium • Baton Rouge, LA (Rivalry)", "ESPN", "W 35–17", "75,453"], ["November 19", "#6 Auburn", "#4", "Legion Field • Birmingham, AL (Iron Bowl)", "ABC", "W 21–14", "83,091"], ["January 2, 1995", "vs. #13 Ohio State*", "#6", "Citrus Bowl • Orlando, FL (Florida Citrus Bowl)", "ABC", "W 24–17", "71,195"], ["September 17", "at Arkansas", "#12", "Razorback Stadium • Fayetteville, AR", "ABC", "W 13–6", "52,089"], ["November 12", "at #20 Mississippi State", "#6", "Scott Field • Starkville, MS (Rivalry)", "ABC", "W 29–25", "41,358"], ["September 24", "Tulane*", "#11", "Legion Field • Birmingham, AL", "", "W 20–10", "81,421"], ["September 3", "Tennessee–Chattanooga*", "#11", "Legion Field • Birmingham, AL", "", "W 42–13", "82,109"], ["October 15", "at Tennessee", "#10", "Neyland Stadium • Knoxville, TN (Third Saturday in October)", "ESPN", "W 17–13", "96,856"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many games where there at least 70,000 people in attendance for the 1994 alabama crimson tide football team? | 11 | 128 | Answer: |
Table InputTable: [["Client", "Years of Operation", "Area of Operation", "Country", "Services"], ["Lasmo", "1993-94", "Wadi Borjuj", "Libya", "Drilling, workover"], ["Agoco", "1991-current", "Sarir field", "Libya", "Drilling, workover"], ["Total", "1999-current", "El Mabrouk", "Libya", "Drilling"], ["Waha", "1994", "El Zahra", "Libya", "Drilling, workover"], ["Fina", "1997", "El Hamada", "Libya", "Drilling, workover"], ["SOC", "2000-current", "SOC fields", "Libya", "Drilling"], ["OMV", "1997", "Field 103", "Libya", "Drilling, workover"], ["Veba", "2000", "Different fields", "Libya", "Drilling, workover"], ["Perenco Oil Co.", "2000-01", "EchiraX Concession", "Gabon", "Drilling"], ["Khalda/Repsol", "1998-99", "West Desert", "Egypt", "Drilling"], ["Agiba-Agip", "1999", "West Desert", "Egypt", "Drilling"], ["Zueitina", "2001-current", "Field 103", "Libya", "Drilling, workover"], ["Marathon", "1998", "Manzala field", "Egypt", "Drilling"], ["IPLL", "1999-current", "El Naka field", "Libya", "Drilling"], ["IPC-Dublin", "1997-97", "Kilwa", "Tanzania", "Drilling"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which country has the greatest number of challenger operations? | Libya | 128 | Answer: |
Table InputTable: [["Team", "City", "Years active", "Seasons played", "Win–loss record", "Win%", "Playoffs appearances"], ["Chicago Stags", "Chicago, Illinois", "1946–1950", "4", "145–92", ".612", "4"], ["Baltimore Bullets*", "Baltimore, Maryland", "1947–1954", "8", "158–292", ".351", "3"], ["Indianapolis Jets", "Indianapolis, Indiana", "1948–1949", "1", "18–42", ".300", "0"], ["Pittsburgh Ironmen", "Pittsburgh, Pennsylvania", "1946–1947", "1", "15–45", ".250", "0"], ["Detroit Falcons", "Detroit, Michigan", "1946–1947", "1", "20–40", ".333", "0"], ["St. Louis Bombers", "St. Louis, Missouri", "1946–1950", "4", "122–115", ".515", "3"], ["Toronto Huskies", "Toronto, Ontario", "1946–1947", "1", "22–38", ".367", "0"], ["Waterloo Hawks", "Waterloo, Iowa", "1949–1950", "1", "19–43", ".306", "0"], ["Denver Nuggets", "Denver, Colorado", "1949–1950", "1", "11–51", ".177", "0"], ["Cleveland Rebels", "Cleveland, Ohio", "1946–1947", "1", "30–30", ".500", "1"], ["Indianapolis Olympians", "Indianapolis, Indiana", "1949–1953", "4", "132–137", ".491", "4"], ["BAA Indianapolis", "Indianapolis, Indiana", "Never Played", "0", "0–0", "N/A", "0"], ["Anderson Packers", "Anderson, Indiana", "1949–1950", "1", "37–27", ".578", "1"], ["BAA Buffalo", "Buffalo, New York", "Never Played", "0", "0–0", "N/A", "0"], ["Providence Steamrollers", "Providence, Rhode Island", "1946–1949", "3", "46–122", ".274", "0"], ["Sheboygan Red Skins", "Sheboygan, Wisconsin", "1949–1950", "1", "22–40", ".355", "1"], ["Washington Capitols", "Washington, D.C.", "1946–1951", "5", "157–114", ".579", "4"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the total of seasons played between baltimore bullets and chicago stags? | 12 | 128 | Answer: |
Table InputTable: [["Binary", "Octal", "Decimal", "Hexadecimal", "Glyph"], ["0011 0111", "067", "55", "37", "7"], ["0011 0100", "064", "52", "34", "4"], ["0011 0010", "062", "50", "32", "2"], ["0011 0110", "066", "54", "36", "6"], ["0011 0101", "065", "53", "35", "5"], ["0011 0011", "063", "51", "33", "3"], ["0011 0001", "061", "49", "31", "1"], ["0011 0000", "060", "48", "30", "0"], ["0011 1000", "070", "56", "38", "8"], ["0011 1001", "071", "57", "39", "9"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the next octal number after 067? | 070 | 128 | Answer: |
Table InputTable: [["Pick #", "Player", "Position", "Nationality", "NHL team", "College/junior/club team"], ["165", "Sean Whyte", "Right Wing", "Canada", "Los Angeles Kings", "Guelph Platers (OHL)"], ["157", "Raymond Saumier", "Right Wing", "Canada", "Hartford Whalers", "Trois Rivieres Draveurs (QMJHL)"], ["152", "Sergei Starikov", "Defense", "Soviet Union", "New Jersey Devils", "CSKA Moscow (USSR)"], ["166", "Dean Holoien", "Right Wing", "Canada", "Washington Capitals", "Saskatoon Blades (WHL)"], ["155", "Rob Sangster", "Left Wing", "Canada", "Vancouver Canucks", "Kitchener Rangers (OHL)"], ["163", "David Shute", "Left Wing", "United States", "Pittsburgh Penguins", "Victoria Cougars (WHL)"], ["159", "Sverre Sears", "Defense", "United States", "Philadelphia Flyers", "Princeton University (NCAA)"], ["167", "Patrick Lebeau", "Left Wing", "Canada", "Montreal Canadiens", "St-Jean Lynx (QMJHL)"], ["164", "Rick Allain", "Defense", "Canada", "Boston Bruins", "Kitchener Rangers (OHL)"], ["153", "Milan Tichy", "Defense", "Czechoslovakia", "Chicago Blackhawks", "Prince Albert Raiders (WHL)"], ["168", "Kevin Wortman", "Defense", "United States", "Calgary Flames", "American International College (NCAA)"], ["158", "Andy Suhy", "Defense", "United States", "Detroit Red Wings", "Western Michigan University (NCAA)"], ["150", "Derek Langille", "Defense", "Canada", "Toronto Maple Leafs", "North Bay Centennials (OHL)"], ["162", "Darcy Martini", "Defense", "Canada", "Edmonton Oilers", "Michigan Technological University (NCAA)"], ["151", "Jim Solly", "Left Wing", "Canada", "Winnipeg Jets", "Bowling Green State University (NCAA)"], ["160", "Greg Spenrath", "Defense", "Canada", "New York Rangers", "Tri-City Americans (WHL)"], ["149", "Phil Huber", "Center", "Canada", "New York Islanders", "Kamloops Blazers (WHL)"], ["148", "Paul Krake", "Goalie", "Canada", "Quebec Nordiques", "University of Alaska Anchorage (NCAA)"], ["161", "Derek Plante", "Center", "United States", "Buffalo Sabres", "Cloquet High School (USHS-MN)"], ["154", "Jon Pratt", "Left Wing", "United States", "Minnesota North Stars", "Pingree High School (USHS-MA)"], ["156", "Kevin Plager", "Right Wing", "United States", "St. Louis Blues", "Parkway North High School (USHS-MO)"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:total number of countries represented in round 8 | 4 | 128 | Answer: |
Table InputTable: [["Name", "Sport", "Event", "Placing", "Performance"], ["Mark Slavin", "Wrestling", "Greco-Roman — Middleweight <82 kg", "—", "(taken hostage before his scheduled event)"], ["Itzhak Nir", "Sailing", "Flying Dutchman", "23", "28-22-22-19-25-19-DNS = 171 pts\\n(left Kiel before 7th race)"], ["Gad Tsobari", "Wrestling", "Freestyle — Light Flyweight <48 kg", "Group stage", "0W–2L"], ["Esther Shahamorov", "Athletics", "Women's 100 m hurdles", "Semifinal", "Did not start (left Munich before the semifinal)"], ["Yehuda Weissenstein", "Fencing", "Men's foil", "Second round", "W2–L8 (1R 2-3, 2R 0-5)"], ["Shlomit Nir", "Swimming", "Women's 200 m breaststroke", "Heats (6th)", "2:53.60"], ["Shlomit Nir", "Swimming", "Women's 100 m breaststroke", "Heats (8th)", "1:20.90"], ["Eliezer Halfin", "Wrestling", "Freestyle — Lightweight <68 kg", "Group stage", "1W–2L"], ["Esther Shahamorov", "Athletics", "Women's 100 m", "Semifinal (5th)", "11.49"], ["David Berger", "Weightlifting", "Light-heavyweight <82.5 kg", "—", "J:132.5 C:122.5 S:— T:—"], ["Yair Michaeli", "Sailing", "Flying Dutchman", "23", "28-22-22-19-25-19-DNS = 171 pts\\n(left Kiel before 7th race)"], ["Shaul Ladani", "Athletics", "Men's 50 km walk", "19", "4:24:38.6\\n(also entered for 20 km walk, but did not start)"], ["Henry Hershkowitz", "Shooting", "50 metre rifle three positions", "46", "1114/1200"], ["Zelig Stroch", "Shooting", "50 metre rifle prone", "57", "589/600"], ["Henry Hershkowitz", "Shooting", "50 metre rifle prone", "23", "593/600"], ["Ze'ev Friedman", "Weightlifting", "Bantamweight <56 kg", "12", "J:102.5 C:102.5 S:125 T:330"], ["Dan Alon", "Fencing", "Men's foil", "Second round", "W5–L5 (1R 3-2, 2R 2-3)"], ["Yossef Romano", "Weightlifting", "Middleweight <75 kg", "—", "(retired injured on third attempt to press 137.5kg)"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:did mark slavin and itzhak nir both complete their events successfully? | no | 128 | Answer: |
Table InputTable: [["Year", "Injuries (US $000)", "Deaths (age <15)", "CPSC toy safety funding\\n(US$ Millions)", "Toy sales\\n(US $ Billions)"], ["1996", "130", "", "", ""], ["1995", "139", "", "", ""], ["1997", "141", "", "", ""], ["2009", "no data", "12", "no data", ""], ["1998", "153", "14", "", ""], ["1994", "154", "", "", ""], ["2007", "no data", "22", "no data", ""], ["2008", "no data", "19", "no data", ""], ["2006", "no data", "22", "no data†", "22.3"], ["2005", "202 (estimate)", "20", "11.0", "22.2"], ["2001", "255", "25", "12.4", ""], ["1999", "152", "16", "13.6", ""], ["2003", "206", "11", "12.8", "20.7"], ["2000", "191", "17", "12.0", ""], ["2004", "210", "16", "11.5", "22.4"], ["2002", "212", "13", "12.2", "21.3"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many injuries were reported in the year that toy safety funding was reported at its lowest? | 202 (estimate) | 128 | Answer: |
Table InputTable: [["Num", "Pod Color", "Nickname", "Short Description", "Episode"], ["090", "", "Fetchit", "This experiment was activated when Mrs. Hasagawa's cats were. Function unknown.", "220"], ["014", "White", "Kernel", "A tan gourd-shaped experiment with a large opening at the top of his head. Designed to pop popcorn. His one true place is in a movie theater. Was mentioned in \"Angel\" when Jumba said \"624 is harmless early experiment. Designed to...pop popcorn for Jumba's movie night.\"", "Leroy & Stitch"], ["049", "White", "Picker", "This experiment was seen in pod form in Stitch! The Movie. His pod says 49 instead of 049, possibly due to the angle. Function unknown.", "Stitch! The Movie"], ["054", "Blue", "Fudgy", "An experiment made of chocolate that looks like a blob. Designed to drown people in his sticky sweetness. When he was activated, he was called 119, and he was mistaken for experiment 611. The mistake with his number was due to Jumba's untidy database, although Jumba later corrected this mistake. Was rescued in \"Snafu.\"", "119, 226"], ["074", "White", "Welco", "This experiment was seen in pod form in Stitch! The Movie. Pod says 74 instead of 074.", "Stitch! The Movie"], ["086", "", "Clink", "A big green mouthless crab-like experiment with four legs, two large claws and a window on its chest. Able to capture and confine any other experiment inside the holding tank in his stomach by splitting in half, surrounding whatever he wants to catch, and joining together again. When Clink splits in two, he works with himself, yet he seems to have a separate mind for each half. 20 years in the future, Lilo, Stitch, and Skip encountered Clink in the possession of Hämsterviel, when Hämsterviel ruled Earth.", "206"], ["024", "Purple", "Hamlette", "An experiment designed to turn objects into ham. She was activated when Mrs. Hasagawa's cats were activated, as indicated by Gantu's experiment computer, but did not physically appear in the episode. She was referred to in Remmy when Pleakley said, \"Is that the one that turns everything into ham?\"", "215, 220"], ["001", "Blue", "Shrink", "A small purple experiment with a white lower jaw and chest, three wobbly legs, two stubby little arms and two floppy antennae with two rings on each antenna. Designed to zap a green ray from his antennas to change the size of objects. His picture appears on the wall of Jumba's lab in Leroy & Stitch, along with several other pictures of Jumba and Dr. Hämsterviel's early accomplishments.", "Leroy & Stitch"], ["036", "", "Poki", "A small yellow and brown opossum-like experiment with a spiked tail. Designed to poke holes in liquid containers. Was seen in \"Shoe.\"", "203, 215, Leroy & Stitch"], ["062", "White", "Frenchfry", "A small gray and white mustached experiment with four arms with three fingers on each hand, black eyes, a round nose, a little mouth, short ears, a chef's hat, and a spatula for a tail. Designed to use his lightning speed, which allows him to instantly prepare food or whip up a mini-tornado in battle. He is also the only experiment in the series that speaks French. Designed to be Jumba's personal chef, but instead made unhealthy food that quickly made people fat, then ate the fattened victims. However, he stopped when he learned that healthy food could be just as delicious. His one true place is running a healthy French fry hut.", "202, Leroy & Stitch"], ["089", "", "Skip", "A purple hourglass-shaped experiment with small eyes. Designed to skip time by 10 minutes, because Jumba was too impatient to wait for his microwave to reheat his leftovers. However, there was an error in his program: he skips time by ten years. Fortunately, he has a reset button. Lilo used him to jump ahead 10 years and become a teenager, then 20 years later to become a full-fledged adult. However, since Lilo and Stitch were gone for 20 years, no one was there to catch experiments except Gantu. So 20 years later, Hämsterviel ruled Earth.", "206, Leroy & Stitch"], ["069", "", "H. T.", "", "Leroy & Stitch"], ["003", "", "Howcome", "", "Leroy & Stitch"], ["031", "", "Gotchu", "An orange lobster-like experiment. Designed to run around and pinches things with his four pincers. His one true place is with Mrs. Hasagawa as one of her \"cats.\"", "220, Leroy & Stitch"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many total experiments are there? | 99 | 128 | Answer: |
Table InputTable: [["Type", "Construction period", "Cylinder", "Capacity", "Power", "Vmax"], ["C2 (10/28 PS)", "1913–1914", "straight-4", "2.412 cc", "28 PS (20,6 kW)", "75 km/h (47 mph)"], ["V 5 Sport", "1931–1932", "V4", "1.168 cc", "30 PS (22 kW)", "100 km/h (62 mph)"], ["V 5", "1931–1932", "V4", "1.168 cc", "25 PS (18,4 kW)", "80 km/h (50 mph)"], ["B6 (9/22 PS)", "1912–1914", "straight-4", "4.900 cc", "45 PS (33 kW)", "95 km/h (59 mph)"], ["D7 (42/120 PS)", "1919–1921", "straight-6", "11.160 cc", "120 PS (88 kW)", "160 km/h (99 mph)"], ["D12 (12/45 PS)", "1923–1924", "straight-6", "3.107 cc", "45 PS (33 kW)", "100 km/h (62 mph)"], ["R 140", "1932–1933", "straight-4", "1.355 cc", "30 PS (22 kW)", "85 km/h (53 mph)–105 km/h (65 mph)"], ["R 140", "1933–1934", "straight-4", "1.466 cc", "30 PS (22 kW)", "85 km/h (53 mph)–105 km/h (65 mph)"], ["C1 (6/18 PS)", "1909–1915", "straight-4", "1.546 cc", "18 PS (13,2 kW)", "70 km/h (43 mph)"], ["B1 (6/16 PS)", "1910–1912", "straight-4", "1.556 cc", "16 PS (11,8 kW)", "65 km/h (40 mph)"], ["D12V (13/55 PS)", "1925–1928", "straight-6", "3.386 cc", "55 PS (40 kW)", "100 km/h (62 mph)"], ["D2 (6/18 PS)", "1919–1920", "straight-4", "1.593 cc", "18 PS (13,2 kW)", "70 km/h (43 mph)"], ["D9 (8/32 PS)", "1923–1924", "straight-4", "2.290 cc", "32 PS (23,5 kW)", "90 km/h (56 mph)"], ["Arkona", "1937–1940", "straight-6", "3.610 cc", "80 PS (59 kW)", "120 km/h (75 mph)–140 km/h (87 mph)"], ["Greif V8", "1934–1937", "V8", "2.489 cc", "55 PS (40 kW)", "110 km/h (68 mph)"], ["Gigant G 15 (15/80 PS)", "1928–1933", "straight-8", "3.974 cc", "80 PS (59 kW)", "100 km/h (62 mph)"], ["D6 (19/55 PS)", "1919–1921", "straight-6", "4.960 cc", "55 PS (40 kW)", "100 km/h (62 mph)"], ["D9V (9/32 PS)", "1925–1927", "straight-4", "2.290 cc", "32 PS (23,5 kW)", "90 km/h (56 mph)"], ["PK4 (11/20 PS)", "1909–1912", "straight-4", "2.544 cc", "20 PS (14,7 kW)", "70 km/h (43 mph)"], ["8 Typ G 14 (14/70 PS)", "1928", "straight-8", "3.633 cc", "70 PS (51 kW)", "100 km/h (62 mph)"], ["D3 (8/24 PS)", "1920–1923", "straight-4", "2.120 cc", "24 PS (17,6 kW)", "70 km/h (43 mph)"], ["Greif V8 Sport", "1935–1937", "V8", "2.489 cc", "57 PS (42 kW)", "120 km/h (75 mph)"], ["Gigant G 15 K (15/80 PS)", "1928–1933", "straight-8", "3.974 cc", "80 PS (59 kW)", "110 km/h (68 mph)"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many kilometers per hour is the vmax of the passenger car model constructed in 1913-1914? | 75 km/h (47 mph) | 128 | Answer: |
Table InputTable: [["Week", "Date", "Opponent", "Result", "Attendance"], ["5", "October 4, 1981", "Cincinnati Bengals", "W 17–10", "44,350"], ["9", "November 1, 1981", "at Cincinnati Bengals", "L 34–21", "54,736"], ["8", "October 26, 1981", "at Pittsburgh Steelers", "L 26–13", "52,732"], ["16", "December 20, 1981", "Pittsburgh Steelers", "W 21–20", "41,056"], ["6", "October 11, 1981", "Seattle Seahawks", "W 35–17", "42,671"], ["2", "September 13, 1981", "at Cleveland Browns", "W 9–3", "79,483"], ["14", "December 3, 1981", "Cleveland Browns", "W 17–13", "44,502"], ["15", "December 13, 1981", "at San Francisco 49ers", "L 28–6", "55,707"], ["10", "November 8, 1981", "Oakland Raiders", "W 17–16", "45,519"], ["1", "September 6, 1981", "at Los Angeles Rams", "W 27–20", "63,198"], ["7", "October 18, 1981", "at New England Patriots", "L 38–10", "60,474"], ["13", "November 29, 1981", "Atlanta Falcons", "L 31–27", "40,201"], ["11", "November 15, 1981", "at Kansas City Chiefs", "L 23–10", "73,984"], ["3", "September 20, 1981", "Miami Dolphins", "L 16–10", "47,379"], ["4", "September 27, 1981", "at New York Jets", "L 33–17", "50,309"], ["12", "November 22, 1981", "New Orleans Saints", "L 27–24", "49,581"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many points did they beat the bengals by in week 5? | 7 | 128 | Answer: |
Table InputTable: [["Rank", "Pair", "Name", "Country", "Time", "Time behind"], ["4", "20", "Lee Kyou-hyuk", "South Korea", "1:09.37", "+0.48"], ["", "21", "Erben Wennemars", "Netherlands", "1:09.32", "+0.43"], ["24", "17", "Mun Jun", "South Korea", "1:10.66", "+1.77"], ["–", "2", "Ermanno Ioriatti", "Italy", "DQ", "–"], ["33", "1", "An Weijiang", "China", "1:11.80", "+2.91"], ["8", "18", "Stefan Groothuis", "Netherlands", "1:09.57", "+0.68"], ["12", "9", "Beorn Nijenhuis", "Netherlands", "1:09.85", "+0.96"], ["10", "6", "Dmitry Dorofeyev", "Russia", "1:09.74", "+0.85"], ["36", "8", "Aleksandr Zhigin", "Kazakhstan", "1:12.36", "+3.47"], ["5", "21", "Jan Bos", "Netherlands", "1:09.42", "+0.53"], ["34", "1", "Yu Fengtong", "China", "1:11.90", "+3.01"], ["17", "11", "Choi Jae-bong", "South Korea", "1:10.23", "+1.34"], ["37", "9", "Risto Rosendahl", "Finland", "1:12.60", "+3.71"], ["21", "15", "Aleksandr Kibalko", "Russia", "1:10.50", "+1.61"], ["20", "15", "Yusuke Imai", "Japan", "1:10.48", "+1.59"], ["25", "10", "Janne Hänninen", "Finland", "1:10.83", "+1.94"], ["22", "10", "Lee Kang-seok", "South Korea", "1:10.52", "+1.63"], ["30", "8", "Maurizio Carnino", "Italy", "1:11.44", "+2.55"], ["31", "1", "Pekka Koskela", "Finland", "1:11.45", "+2.56"], ["", "20", "Joey Cheek", "United States", "1:09.16", "+0.27"], ["–", "5", "Maciej Ustynowicz", "Poland", "DQ", "–"], ["15", "13", "Alexey Proshin", "Russia", "1:10.14", "+1.25"], ["13", "4", "Konrad Niedźwiedzki", "Poland", "1:09.95", "+1.06"], ["7", "16", "Yevgeny Lalenkov", "Russia", "1:09.46", "+0.57"], ["27", "14", "Takaharu Nakajima", "Japan", "1:11.10", "+2.21"], ["28", "3", "Takahiro Ushiyama", "Japan", "1:11.21", "+2.32"], ["38", "5", "Lu Zhuo", "China", "1:12.69", "+3.80"], ["35", "7", "Zhang Zhongqi", "China", "1:12.29", "+3.40"], ["18", "12", "Petter Andersen", "Norway", "1:10.38", "+1.38"], ["23", "14", "Even Wetten", "Norway", "1:10.57", "+1.68"], ["16", "7", "François-Olivier Roberge", "Canada", "1:10.20", "+1.31"], ["32", "1", "Keiichiro Nagashima", "Japan", "1:11.78", "+2.89"], ["26", "2", "Mika Poutala", "Finland", "1:11.03", "+2.14"], ["–", "6", "Erik Zachrisson", "Sweden", "DQ", "–"], ["", "19", "Shani Davis", "United States", "1:08.89", ""], ["9", "18", "Casey FitzRandolph", "United States", "1:09.59", "+0.70"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what country is at the top? | United States | 128 | Answer: |
Table InputTable: [["Rank", "Country", "Box Office", "Year", "Box office\\nfrom national films"], ["6", "South Korea", "$1.47 billion", "2013", "59.7% (2013)"], ["3", "Japan", "$1.88 billion", "2013", "61% (2013)"], ["5", "France", "$1.7 billion", "2012", "33.3% (2013)"], ["7", "India", "$1.4 billion", "2012", "–"], ["2", "China", "$3.6 billion", "2013", "59% (2013)"], ["12", "Brazil", "$0.72 billion", "2013", "17% (2013)"], ["8", "Germany", "$1.3 billion", "2012", "–"], ["11", "Italy", "$0.84 billion", "2013", "30% (2013)"], ["4", "United Kingdom", "$1.7 billion", "2012", "36.1% (2011)"], ["-", "World", "$34.7 billion", "2012", "–"], ["9", "Russia", "$1.2 billion", "2012", "–"], ["10", "Australia", "$1.2 billion", "2012", "4.1% (2011)"], ["1", "Canada/United States", "$10.8 billion", "2012", "–"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many asian countries received over 1.5 billion dollars in box office revenue in 2013? | 2 | 128 | Answer: |
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