Split (1)

test · 4.34k rows

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: [["Poll Source", "Sample Size", "Margin of Error", "Date", "Democrat", "%", "Republican", "%"], ["Survey USA", "563", "4.2", "Feb 15-17, 2008", "Hillary Clinton", "41", "John McCain", "52"], ["Rasmussen Reports", "500", "", "Feb 18, 2008", "Barack Obama", "44", "John McCain", "41"], ["Rasmussen Reports", "500", "", "Feb 18, 2008", "Hillary Clinton", "37", "John McCain", "47"], ["Survey USA", "517", "4.4", "Mar 14-16, 2008", "Hillary Clinton", "44", "John McCain", "48"], ["Survey USA", "563", "4.2", "Feb 15-17, 2008", "Barack Obama", "51", "John McCain", "41"], ["Rasmussen Reports", "500", "4.5", "Mar 31, 2008", "Hillary Clinton", "36", "John McCain", "51"], ["Rasmussen Reports", "500", "4.5", "Mar 31, 2008", "Barack Obama", "46", "John McCain", "42"], ["Survey USA", "543", "4.3", "Jan 4-6, 2008", "Hillary Clinton", "44", "John McCain", "48"], ["Survey USA", "546", "4.3", "Nov 9-11, 2007", "Hillary Clinton", "44", "John McCain", "48"], ["Survey USA", "517", "4.4", "Mar 14-16, 2008", "Barack Obama", "50", "John McCain", "44"], ["Survey USA", "502", "4.5", "Oct 12-14, 2007", "Hillary Clinton", "49", "John McCain", "44"], ["Survey USA", "498", "4.5", "Oct 12-14, 2007", "Hillary Clinton", "52", "Ron Paul", "36"], ["Survey USA", "546", "4.3", "Nov 9-11, 2007", "Barack Obama", "50", "John McCain", "42"], ["Survey USA", "543", "4.3", "Jan 4-6, 2008", "Barack Obama", "55", "John McCain", "38"], ["Survey USA", "546", "4.3", "Nov 9-11, 2007", "Barack Obama", "53", "Mitt Romney", "39"], ["Survey USA", "506", "4.4", "Oct 12-14, 2007", "Hillary Clinton", "50", "Mitt Romney", "42"], ["Survey USA", "546", "4.3", "Nov 9-11, 2007", "Hillary Clinton", "49", "Mitt Romney", "43"], ["Survey USA", "543", "4.3", "Jan 4-6, 2008", "Hillary Clinton", "48", "Mitt Romney", "40"], ["Survey USA", "509", "4.4", "Oct 12-14, 2007", "Hillary Clinton", "50", "Fred Thompson", "42"], ["Survey USA", "546", "4.3", "Nov 9-11, 2007", "Hillary Clinton", "49", "Mike Huckabee", "43"], ["Survey USA", "543", "4.3", "Jan 4-6, 2008", "Barack Obama", "59", "Mitt Romney", "33"], ["Survey USA", "543", "4.3", "Jan 4-6, 2008", "Hillary Clinton", "51", "Rudy Giuliani", "35"], ["Survey USA", "546", "4.3", "Nov 9-11, 2007", "Barack Obama", "56", "Mike Huckabee", "35"], ["Survey USA", "543", "4.3", "Jan 4-6, 2008", "Hillary Clinton", "47", "Mike Huckabee", "45"], ["Survey USA", "543", "4.3", "Jan 4-6, 2008", "Barack Obama", "58", "Mike Huckabee", "35"], ["Survey USA", "513", "4.4", "Oct 12-14, 2007", "Hillary Clinton", "48", "Rudy Giuliani", "43"], ["Survey USA", "506", "4.3", "Oct 12-14, 2007", "Hillary Clinton", "51", "Mike Huckabee", "41"]]	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:give the number of poll sources listed here.	2	128	Answer:
Table InputTable: [["Political lieutenant", "District\\n(Area)", "Took Office", "Left Office", "Party leader"], ["Benoît Bouchard", "Roberval\\n(Saguenay-Lac-Saint-Jean)", "1990", "1993", "Brian Mulroney"], ["Lucien Bouchard", "Lac-Saint-Jean\\n(Saguenay-Lac-Saint-Jean)", "1988", "1990", "Brian Mulroney"], ["Léon Balcer", "Trois-Rivières\\n(Mauricie)", "1957", "1965", "John George Diefenbaker"], ["Georges-Henri Héon", "Argenteuil\\n(Laurentides)", "1949", "1949", "George A. Drew"], ["Claude Wagner", "Saint-Hyacinthe\\n(Montérégie)", "1972", "1978", "Robert Stanfield\\nJoe Clark"], ["André Bachand", "Richmond—Arthabaska\\n(Centre-du-Québec &\\nEastern Townships)", "1998", "2004", "Joe Clark\\nPeter MacKay"], ["Monique Landry", "Blainville—Deux-Montagnes\\n(Laurentides)", "1993", "1993", "Kim Campbell"], ["Marcel Faribault", "none", "1967", "1968", "Robert Stanfield"]]	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 party leader of lucien bouchard and benoit bouchard?	Brian Mulroney	128	Answer:
Table InputTable: [["Result", "Date", "Category", "Tournament", "Surface", "Partnering", "Opponents", "Score"], ["Winner", "27 November 1983", "$150,000", "Sydney, Australia", "Grass", "Wendy Turnbull", "Hana Mandlíková\\n Helena Suková", "6–4, 6–3"], ["Runner-up", "19 June 1983", "$150,000", "Eastbourne, Great Britain", "Grass", "Jo Durie", "Martina Navrátilová\\n Pam Shriver", "1–6, 0–6"], ["Runner-up", "29 January 1984", "$100,000", "Marco Island, United States", "Clay", "Andrea Jaeger", "Hana Mandlíková\\n Helena Suková", "6–3, 2–6, 2–6"], ["Runner-up", "8 November 1981", "$50,000", "Hong Kong", "Clay", "Susan Leo", "Ann Kiyomura\\n Sharon Walsh", "3–6, 4–6"], ["Runner-up", "12 December 1983", "Grand Slam", "Australian Open, Australia", "Grass", "Wendy Turnbull", "Martina Navrátilová\\n Pam Shriver", "4–6, 7–6, 2–6"], ["Winner", "20 November 1983", "$150,000", "Brisbane, Australia", "Grass", "Wendy Turnbull", "Pam Shriver\\n Sharon Walsh", "6–3, 6–4"], ["Runner-up", "9 September 1984", "Grand Slam", "US Open, United States", "Hard", "Wendy Turnbull", "Martina Navrátilová\\n Pam Shriver", "2–6, 4–6"], ["Runner-up", "23 April 1984", "$200,000", "Orlando, United States", "Clay", "Wendy Turnbull", "Claudia Kohde-Kilsch\\n Hana Mandlíková", "0–6, 6–1, 3–6"], ["Runner-up", "10 December 1978", "$75,000", "Sydney, Australia", "Grass", "Judy Chaloner", "Kerry Reid\\n Wendy Turnbull", "2–6, 6–4, 2–6"], ["Winner", "13 June 1982", "$100,000", "Birmingham, Great Britain", "Grass", "Jo Durie", "Rosie Casals\\n Wendy Turnbull", "6–3, 6–2"], ["Winner", "23 May 1983", "$150,000", "Berlin, Germany", "Carpet", "Jo Durie", "Claudia Kohde-Kilsch\\n Eva Pfaff", "6–4, 7–6(7–2)"], ["Runner-up", "16 April 1984", "$200,000", "Hilton Head, United States", "Clay", "Sharon Walsh", "Claudia Kohde-Kilsch\\n Hana Mandlíková", "5–7, 2–6"], ["Runner-up", "19 July 1987", "$150,000", "Newport, United States", "Grass", "Kathy Jordan", "Gigi Fernández\\n Lori McNeil", "6–7(5–7), 5–7"], ["Winner", "15 December 1985", "$50,000", "Auckland, New Zealand", "Grass", "Candy Reynolds", "Lea Antonoplis\\n Adriana Villagrán", "6–1, 6–3"], ["Runner-up", "22 April 1984", "$250,000", "Amelia Island, United States", "Clay", "Mima Jaušovec", "Kathy Jordan\\n Anne Smith", "4–6, 6–3, 4–6"], ["Winner", "20 May 1984", "$150,000", "Berlin, Germany", "Clay", "Candy Reynolds", "Kathy Horvath\\n Virginia Ruzici", "6–3, 4–6, 7–6(13–11)"], ["Winner", "21 August 1983", "$200,000", "Toronto, Canada", "Hard", "Andrea Jaeger", "Rosalyn Fairbank\\n Candy Reynolds", "6–4, 5–7, 7–5"], ["Runner-up", "30 August 1987", "$150,000", "Mahwah, United States", "Hard", "Liz Smylie", "Gigi Fernández\\n Lori McNeil", "3–6, 2–6"]]	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 tournaments were held in the month of november?	3	128	Answer:
Table InputTable: [["Week", "Date", "TV Time", "Opponent", "Result", "Attendance"], ["1", "September 6, 1998", "FOX 1:05 pm MT", "at Dallas Cowboys", "L 38–10", "63,602"], ["4", "September 27, 1998", "FOX 10:00 am MT", "at St. Louis Rams", "W 20–17", "55,832"], ["2", "September 13, 1998", "FOX 1:15 pm MT", "at Seattle Seahawks", "L 33–14", "57,678"], ["9", "November 1, 1998", "FOX 11:00 am MT", "at Detroit Lions", "W 17–15", "66,087"], ["10", "November 8, 1998", "FOX 2:15 pm MT", "Washington Redskins", "W 29–27", "45,950"], ["13", "November 29, 1998", "FOX 11:00 am MT", "at Kansas City Chiefs", "L 34–24", "69,613"], ["3", "September 20, 1998", "ESPN 5:15 pm MT", "Philadelphia Eagles", "W 17–3", "39,782"], ["15", "December 13, 1998", "FOX 11:00 am MT", "at Philadelphia Eagles", "W 20–17", "62,176"], ["16", "December 20, 1998", "FOX 2:05 pm MT", "New Orleans Saints", "W 19–17", "51,617"], ["17", "December 27, 1998", "CBS 2:05 pm MT", "San Diego Chargers", "W 16–13", "71,670"], ["6", "October 11, 1998", "FOX 1:05 pm MT", "Chicago Bears", "W 20–7", "50,495"], ["12", "November 22, 1998", "FOX 11:00 am MT", "at Washington Redskins", "W 45–42", "63,435"], ["11", "November 15, 1998", "FOX 2:15 pm MT", "Dallas Cowboys", "L 35–28", "71,670"], ["5", "October 4, 1998", "CBS 1:05 pm MT", "Oakland Raiders", "L 23–20", "53,240"], ["14", "December 6, 1998", "FOX 2:15 pm MT", "New York Giants", "L 23–19", "46,128"], ["7", "October 18, 1998", "FOX 10:00 am MT", "at New York Giants", "L 34–7", "70,456"], ["8", "Bye", "Bye", "Bye", "Bye", ""]]	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 opponent of the first week in 1998?	at Dallas Cowboys	128	Answer:
Table InputTable: [["Contestant", "Original\\nTribe", "First\\nSwitch", "Second\\nSwitch", "Merged\\nTribe", "Finish", "Ghost\\nIsland", "Total\\nVotes"], ["Aleksandar Bošković\\n28, Belgrade", "Manobo", "Manobo", "", "", "7th Voted Out\\nDay 25", "8th Eliminated\\nDay 27", "4"], ["Luka Rajačić\\n21, Belgrade", "Ga 'dang", "Manobo", "Manobo", "", "9th Voted Out\\nDay 31", "10th Eliminated\\nDay 32", "6"], ["Gordana Berger\\n38, Belgrade", "Manobo", "", "", "", "1st Voted Out\\nDay 4", "2nd Eliminated\\nDay 12", "9"], ["Njegoš Arnautović\\n21, Bijeljina, Republika Srpska", "Manobo", "Ga 'dang", "Ga 'dang", "Diwata", "Eliminated in Challenge\\n7th Jury Member\\nDay 53", "Locator of\\nHidden Immunity Idol\\n(Successful)\\nDay 40", "1"], ["Ana Mitrić\\n23, Belgrade", "Ga 'dang", "", "", "", "3rd Voted Out\\nDay 10", "3rd Eliminated\\nDay 15", "7"], ["Dušan Milisavljević\\n25, Zvečan", "Manobo", "Manobo", "Manobo", "Diwata", "Eliminated in Challenge\\n8th Jury Member\\nDay 53", "Locator of\\nHidden Immunity Idol\\n(Failed)\\nDay 37", "2"], ["Srđan Dinčić\\n25, Sremska Mitrovica", "Manobo", "Manobo", "Manobo", "Diwata", "15th Voted Out\\n6th Jury Member\\nDay 50", "", "7"], ["Ana Stojanovska\\n21, Skopje, Macedonia", "Manobo", "Manobo", "Manobo", "", "8th Voted Out\\nDay 28", "9th Eliminated\\nDay 30", "3"], ["Višnja Banković\\n24, Aranđelovac", "Ga 'dang", "Ga 'dang", "Ga 'dang", "Diwata", "13th Voted Out\\n4th Jury Member\\nDay 44", "", "14"], ["Srđan Dinčić\\n25, Sremska Mitrovica", "Manobo", "Manobo", "Ga 'dang", "Diwata", "15th Voted Out\\n6th Jury Member\\nDay 50", "", "7"], ["Branislava Bogdanović\\n27, Kačarevo", "Manobo", "", "", "", "Eliminated in a twist\\nDay 17", "5th Eliminated\\nDay 18", "2"], ["Predrag Veljković\\n29, Pekčanica, near Kraljevo", "Ga 'dang", "Manobo", "", "", "Quit\\nDay 22", "7th Eliminated\\nDay 24", "3"], ["Dina Berić\\n23, Ledinci, near Novi Sad", "Manobo", "Ga 'dang", "Manobo", "Diwata", "12th Voted Out\\n3rd Jury Member\\nDay 41", "", "6"], ["Klemen Rutar\\n21, Ljubljana, Slovenija", "Manobo", "Ga 'dang", "Ga 'dang", "Diwata", "14th Voted Out\\n5th Jury Member\\nDay 47", "Locator of\\nHidden Immunity Idol\\n(Failed)\\nDay 34", "6"], ["Nikola Kovačević\\n24, Kragujevac", "Ga 'dang", "", "", "Diwata", "11th Voted Out\\n2nd Jury Member\\nDay 38", "Ghost Island Winner\\nDay 32", "12"], ["Vesna Đolović\\n38, Beograd", "Manobo", "Manobo", "Manobo", "Diwata", "2nd Runner-Up", "Locator of\\nHidden Immunity Idol\\n(Failed)\\nDay 46", "8"], ["Pece Kotevski\\n42, Bitola, Macedonia", "Ga 'dang", "Manobo", "", "", "6th Voted Out\\nDay 19", "6th Eliminated\\nDay 21", "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 contestants are from belgrade?	4	128	Answer:
Table InputTable: [["Year", "Competition", "Venue", "Position", "Notes"], ["2006", "World Indoor Championships", "Moscow, Russia", "10th (q)", "5.65 m"], ["1999", "World Youth Championships", "Bydgoszcz, Poland", "13th (q)", "4.60 m"], ["2007", "European Indoor Championships", "Birmingham, United Kingdom", "16th (q)", "5.40 m"], ["2012", "European Championships", "Helsinki, Finland", "–", "NM"], ["2006", "European Championships", "Gothenburg, Sweden", "5th", "5.65 m"], ["2002", "World Junior Championships", "Kingston, Jamaica", "8th", "5.30 m"], ["2003", "European U23 Championships", "Bydgoszcz, Poland", "13th (q)", "5.20 m"], ["2010", "European Championships", "Barcelona, Spain", "3rd", "5.75 m"], ["2001", "European Junior Championships", "Grosseto, Italy", "7th", "5.15 m"], ["2008", "Olympic Games", "Beijing, China", "11th", "5.45 m"], ["2005", "Universiade", "Izmir, Turkey", "5th", "5.50 m"], ["2005", "European U23 Championships", "Erfurt, Germany", "7th", "5.50 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:how many times was a position of at least 10th place or better earned?	7	128	Answer:
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["2", "Japan (JPN)", "46", "56", "77", "179"], ["1", "China (CHN)", "127", "63", "33", "223"], ["3", "South Korea (KOR)", "32", "48", "65", "145"], ["4", "Chinese Taipei (TPE)", "12", "34", "26", "72"], ["7", "Hong Kong (HKG)", "2", "2", "9", "13"], ["6", "North Korea (PRK)", "6", "10", "20", "36"], ["8", "Mongolia (MGL)", "1", "1", "6", "8"], ["5", "Macau (MAC)", "11", "16", "17", "44"], ["9", "Guam (GUM)", "0", "0", "1", "1"], ["Total", "Total", "237", "230", "254", "721"]]	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 medals did china and japan win combined?	402	128	Answer:
Table InputTable: [["Client", "Years of Operation", "Area of Operation", "Country", "Services"], ["Khalda/Repsol", "1998-99", "West Desert", "Egypt", "Drilling"], ["Agiba-Agip", "1999", "West Desert", "Egypt", "Drilling"], ["Lasmo", "1993-94", "Wadi Borjuj", "Libya", "Drilling, workover"], ["Waha", "1994", "El Zahra", "Libya", "Drilling, workover"], ["IPLL", "1999-current", "El Naka field", "Libya", "Drilling"], ["OMV", "1997", "Field 103", "Libya", "Drilling, workover"], ["Agoco", "1991-current", "Sarir field", "Libya", "Drilling, workover"], ["Marathon", "1998", "Manzala field", "Egypt", "Drilling"], ["Fina", "1997", "El Hamada", "Libya", "Drilling, workover"], ["Zueitina", "2001-current", "Field 103", "Libya", "Drilling, workover"], ["Total", "1999-current", "El Mabrouk", "Libya", "Drilling"], ["Veba", "2000", "Different fields", "Libya", "Drilling, workover"], ["IPC-Dublin", "1997-97", "Kilwa", "Tanzania", "Drilling"], ["SOC", "2000-current", "SOC fields", "Libya", "Drilling"], ["Perenco Oil Co.", "2000-01", "EchiraX Concession", "Gabon", "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 previous area of operation comes after west desert?	El Mabrouk	128	Answer:
Table InputTable: [["Year", "Class", "No", "Tyres", "Car", "Team", "Co-Drivers", "Laps", "Pos.", "Class\\nPos."], ["1978", "S\\n+2.0", "10", "", "Mirage M9\\nRenault 2.0L Turbo V6", "Grand Touring Cars Inc.", "Vern Schuppan\\n Sam Posey", "293", "10th", "5th"], ["1977", "S\\n+2.0", "8", "", "Renault Alpine A442\\nRenault 2.0L Turbo V6", "Renault Sport", "Patrick Depailler", "289", "DNF", "DNF"], ["1972", "S\\n3.0", "22", "", "Ligier JS2\\nMaserati 3.0L V6", "Automobiles Ligier", "Pierre Maublanc", "195", "DNF", "DNF"], ["1990", "C1", "6", "G", "Porsche 962C\\nPorsche Type-935 3.0L Turbo Flat-6", "Joest Porsche Racing", "Henri Pescarolo\\n Jean-Louis Ricci", "328", "14th", "14th"], ["1974", "S\\n3.0", "15", "", "Ligier JS2\\nMaserati 3.0L V6", "Automobiles Ligier", "Alain Serpaggi", "310", "8th", "5th"], ["1973", "S\\n3.0", "62", "", "Ligier JS2\\nMaserati 3.0L V6", "Automobiles Ligier", "Guy Ligier", "24", "DSQ", "DSQ"], ["1994", "GT2", "49", "P", "Porsche 911 Carrera RSR\\nPorsche 3.8 L Flat-6", "Larbre Compétition", "Jacques Alméras\\n Jean-Marie Alméras", "94", "DNF", "DNF"], ["1993", "GT", "71", "D", "Venturi 500LM\\nRenault PRV 3.0 L Turbo V6", "Jacadi Racing", "Michel Maisonneuve\\n Christophe Dechavanne", "210", "DNF", "DNF"], ["1996", "GT1", "38", "M", "McLaren F1 GTR\\nBMW S70 6.1L V12", "Team Bigazzi SRL", "Steve Soper\\n Marc Duez", "318", "11th", "9th"]]	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 laps in 1978?	293	128	Answer:
Table InputTable: [["Tablet", "Genealogy", "Narrative", "Colophon"], ["10", "Descendants of Esau 36:2 - 5", "36:6 - 8", "\"This is the account of Esau.\" 36:9"], ["5", "Descendants of Shem, Ham, and Japeth 10:1 - 32", "11:1 - 9", "\"This is the account of Shem.\" 11:10"], ["3", "Adam to Noah 5:1 - 32", "6:1 - 8", "\"This is the account of Noah.\" 6:9"], ["4", "Noah to Shem, Ham, and Japeth 6:9 - 10", "6:11 to 9:29", "\"This is the account of Shem, Ham, and Japheth, Noah's sons.\" 10:1"], ["7", "Terah to Abraham 11:27", "11:28 to 25:11", "\"This is the account of Abraham's son Ishmael.\" 25:12 (eldest son)"], ["9", "Abraham to Isaac 25:19", "25:20 to 35:29", "\"This is the account of Esau.\" 36:1 (eldest son)"], ["1", "Creation of Universe 1:1", "1:2 to 2:3", "\"This is the account of the heavens and of the earth when they were created.\" 2:4"], ["2", "Heavens and Earth 2:4", "2:5 to 4:26", "\"This is the written account of Adam.\" 5:1"], ["6", "Shem to Terah 11:10 - 26", "no narrative", "\"This is the account of Terah.\" 11:27"], ["11", "Descendants of Esau 36:10 to 37:1", "no narrative", "\"This is the account of Jacob.\" 37:2"], ["8", "Descendants of Ishmael 25:13 - 18", "no narrative", "\"This is the account of Abraham's son Isaac.\" 25:19"], ["", "no genealogy", "37:2 to 50:26", "no colophon"]]	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 tablets?	11	128	Answer:
Table InputTable: [["Year", "MVP", "Defensive Player of the Year", "Unsung Hero", "Newcomer of the Year"], ["2010", "Philippe Billy", "Philippe Billy", "Tony Donatelli", "Ali Gerba"], ["2008", "Matt Jordan", "Nevio Pizzolitto", "Joey Gjertsen", "Stefano Pesoli"], ["1993", "Patrice Ferri", "–", "–", "–"], ["2003", "Greg Sutton", "Gabriel Gervais", "David Fronimadis", "Martin Nash"], ["2000", "Jim Larkin", "–", "–", "–"], ["1996", "Paolo Ceccarelli", "–", "–", "–"], ["1995", "Lloyd Barker", "–", "–", "–"], ["1994", "Jean Harbor", "–", "–", "–"], ["2011", "Hassoun Camara", "Evan Bush", "Simon Gatti", "Ian Westlake / Sinisa Ubiparipovic"], ["2002", "Eduardo Sebrango", "Gabriel Gervais", "Jason DiTullio", "Zé Roberto"], ["2001", "Mauro Biello", "–", "–", "–"], ["2007", "Leonardo Di Lorenzo", "Mauricio Vincello", "Simon Gatti", "Matt Jordan"], ["2004", "Gabriel Gervais", "Greg Sutton", "Zé Roberto", "Sandro Grande"], ["1998", "Mauro Biello", "–", "–", "–"], ["1999", "N/A", "–", "–", "–"], ["1997", "Mauro Biello", "–", "–", "–"], ["2009", "David Testo", "Nevio Pizzolitto", "Adam Braz", "Stephen deRoux"], ["2006", "Mauricio Vincello", "Gabriel Gervais", "Andrew Weber", "Leonardo Di Lorenzo"], ["2005", "Mauro Biello", "Nevio Pizzolitto", "Mauricio Vincello", "Masahiro Fukazawa"]]	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 canadian mvps?	9	128	Answer:
Table InputTable: [["Rank\\n", "Date\\n", "Level at Trent Bridge\\nm", "Level at Trent Bridge\\nft", "Peak Flow\\nm3/s", "Peak Flow\\ncfs"], ["1", "February 1795", "24.55", "80.5", "1,416", "50,000"], ["4", "November 1852", "24.26", "79.6", "1,082", "38,200"], ["5", "November 2000", "23.80", "78.1", "1,019", "36,000"], ["3", "March 1947", "24.30", "79.7", "1,107", "39,100"], ["2", "October 1875", "24.38", "80.0", "1,274", "45,000"], ["", "Normal / Avg flow", "20.7", "68", "84", "3,000"]]	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 of the five largest flood on the river trent occurred after 1900?	2	128	Answer:
Table InputTable: [["Player", "Friendlies", "FIFA Confederations Cup", "FIFA World Cup Qual.", "Total Goals"], ["Milicic", "2", "-", "-", "2"], ["Zdrilic", "1", "-", "-", "1"], ["Colosimo", "1", "-", "-", "1"], ["Cahill", "-", "-", "1", "1"], ["Elrich", "1", "-", "-", "1"], ["Griffiths", "1", "-", "-", "1"], ["Bresciano", "2", "-", "1", "3"], ["Chipperfield", "1", "-", "1", "2"], ["Skoko", "-", "1", "-", "1"], ["Viduka", "1", "-", "2", "3"], ["Aloisi", "1", "4", "-", "5"], ["Culina", "-", "-", "1", "1"], ["Thompson", "1", "-", "2", "3"], ["Emerton", "-", "-", "2", "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:players who scored at most 1 total goal	Cahill, Colosimo, Culina, Elrich, Griffiths, Skoko, Zdrilic	128	Answer:
Table InputTable: [["Year", "Title", "Chart positions\\nAU", "Chart positions\\nCA", "Chart positions\\nIE", "Chart positions\\nNL", "Chart positions\\nNZ", "Chart positions\\nUK", "Chart positions\\nUS\\nHot 100", "Chart positions\\nUS\\nAirplay", "Chart positions\\nUS\\nAlternative"], ["1997", "\"All I Really Want\"", "40", "2", "—", "—", "—", "59", "—", "65", "14"], ["1996", "\"Head Over Feet\"", "12", "1", "11", "33", "27", "7", "—", "3", "25"], ["1995", "\"Hand in My Pocket\"", "13", "1", "—", "—", "7", "26", "—", "15", "1"], ["1996", "\"Ironic\"", "3", "1", "8", "6", "3", "11", "4", "2", "1"], ["1995", "\"You Oughta Know\" A", "4", "20", "—", "11", "25", "22", "—", "13", "1"], ["1996", "\"You Learn\"", "20", "1", "—", "13", "—", "24", "6", "1", "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 singles from the chart were played overall?	6	128	Answer:
Table InputTable: [["decimal32", "decimal64", "decimal128", "decimal(32k)", "Format"], ["96", "384", "6144", "Emax = 3×2w−1", "Largest value is 9.99...×10Emax"], ["7", "16", "34", "p = 3×t/10+1 = 9×k−2", "Coefficient size (decimal digits)"], ["192", "768", "12288", "3×2w = 48×4k", "Exponent range"], ["32", "64", "128", "32×k", "Total size (bits)"], ["−95", "−383", "−6143", "Emin = 1−Emax", "Smallest normalized value is 1.00...×10Emin"], ["5", "5", "5", "5", "Combination field (bits)"], ["1", "1", "1", "1", "Sign field (bits)"], ["6", "8", "12", "w = 2×k + 4", "Exponent continuation field (bits)"], ["−101", "−398", "−6176", "Etiny = 2−p−Emax", "Smallest non-zero value is 1×10Etiny"], ["20", "50", "110", "t = 30×k−10", "Coefficient continuation field (bits)"]]	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:name the format with the highest value in "decimal 128".	Exponent range	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"], ["5.", "August 5, 2012", "Manta", "Hard", "Renzo Olivo", "Víctor Estrella\\n João Souza", "6–3, 6–0"], ["6.", "August 20, 2012", "Colombia F2", "Clay", "Ariel Behar", "Nicolas Barrientos\\n Michael Quintero", "2-1 Ret."], ["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"], ["1.", "September 13, 2010", "Ecuador F2", "Hard", "Roberto Quiroz", "Peter Aarts\\n Christopher Racz", "6–4, 6–4"], ["2.", "April 4, 2011", "Chile F2", "Clay", "Sergio Galdós", "Guillermo Hormazábal\\n Rodrigo Pérez", "5–7, 7–6(5), [10–5]"], ["9.", "May 13, 2013", "Argentina F6", "Clay", "Sergio Galdós", "Franco Agamenone\\n Jose Angel Carrizo", "4-6, 6-4, [10–1]"], ["4.", "August 8, 2011", "Peru F1", "Clay", "Sergio Galdós", "Martín Cuevas\\n Guido Pella", "6–4, 6–0"], ["8.", "October 8, 2012", "Chile F8", "Clay", "Gustavo Sterin", "Cristóbal Saavedra-Corvalán\\n Guillermo Rivera-Aránguiz", "6-4, 7-5"]]	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 the next partnering listed after renzo olivo?	Ariel Behar	128	Answer:
Table InputTable: [["Year", "Competition", "Venue", "Position", "Notes"], ["1999", "World Youth Championships", "Bydgoszcz, Poland", "13th (q)", "4.60 m"], ["2001", "European Junior Championships", "Grosseto, Italy", "7th", "5.15 m"], ["2003", "European U23 Championships", "Bydgoszcz, Poland", "13th (q)", "5.20 m"], ["2006", "World Indoor Championships", "Moscow, Russia", "10th (q)", "5.65 m"], ["2006", "European Championships", "Gothenburg, Sweden", "5th", "5.65 m"], ["2005", "European U23 Championships", "Erfurt, Germany", "7th", "5.50 m"], ["2002", "World Junior Championships", "Kingston, Jamaica", "8th", "5.30 m"], ["2007", "European Indoor Championships", "Birmingham, United Kingdom", "16th (q)", "5.40 m"], ["2012", "European Championships", "Helsinki, Finland", "–", "NM"], ["2005", "Universiade", "Izmir, Turkey", "5th", "5.50 m"], ["2010", "European Championships", "Barcelona, Spain", "3rd", "5.75 m"], ["2008", "Olympic Games", "Beijing, China", "11th", "5.45 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:how high was przemyslaw's jump, at the 1999 world youth championships?	4.60 m	128	Answer:
Table InputTable: [["Game", "Date", "Team", "Score", "High points", "High rebounds", "High assists", "Location\\nAttendance", "Record"], ["40", "January 14", "@ Golden State", "W 135–133 (3OT)", "Brad Miller (30)", "Brad Miller (22)", "John Salmons, Beno Udrih (7)", "Oracle Arena\\n19,122", "10–30"], ["33", "January 2", "@ Detroit", "L 92–98", "Brad Miller (25)", "Brad Miller (16)", "John Salmons (4)", "The Palace of Auburn Hills\\n22,076", "8–25"], ["37", "January 9", "Miami", "L 115–119 (OT)", "John Salmons (29)", "Brad Miller (16)", "John Salmons, Brad Miller, Bobby Jackson (4)", "ARCO Arena\\n12,587", "8–29"], ["42", "January 20", "@ Denver", "L 99–118", "Kevin Martin (25)", "Jason Thompson (11)", "John Salmons, Beno Udrih (5)", "Pepsi Center\\n15,164", "10–32"], ["36", "January 6", "@ Chicago", "L 94–99", "Kevin Martin (29)", "Brad Miller (12)", "Beno Udrih (5)", "United Center\\n18,060", "8–28"], ["35", "January 5", "@ New Jersey", "L 90–98", "Kevin Martin (36)", "Kenny Thomas (8)", "Brad Miller (8)", "Izod Center\\n12,314", "8–27"], ["44", "January 24", "@ Milwaukee", "L 104–106", "Kevin Martin (20)", "Brad Miller (13)", "Brad Miller (9)", "Bradley Center\\n15,379", "10–34"], ["46", "January 27", "@ Cleveland", "L 110–117", "Kevin Martin (35)", "Kevin Martin (7)", "Kevin Martin (7)", "Quicken Loans Arena\\n20,562", "10–36"], ["48", "January 30", "Chicago", "L 88–109", "Kevin Martin (27)", "Jason Thompson (12)", "Spencer Hawes, Kevin Martin (3)", "ARCO Arena\\n13,356", "10–38"], ["41", "January 16", "Milwaukee", "L 122–129", "John Salmons, Kevin Martin (24)", "Jason Thompson (11)", "John Salmons (6)", "ARCO Arena\\n11,663", "10–31"], ["34", "January 3", "@ Indiana", "L 117–122", "Kevin Martin (45)", "Bobby Jackson (10)", "Kevin Martin, Brad Miller (6)", "Conseco Fieldhouse\\n12,765", "8–26"], ["38", "January 11", "Dallas", "W 102–95", "Kevin Martin (21)", "Spencer Hawes (8)", "Beno Udrih (6)", "ARCO Arena\\n12,294", "9–29"], ["43", "January 21", "Washington", "L 107–110", "John Salmons, Beno Udrih (24)", "John Salmons, Shelden Williams, Jason Thompson (5)", "John Salmons (5)", "ARCO Arena\\n10,821", "10–33"], ["39", "January 13", "Orlando", "L 107–139", "Kevin Martin (30)", "Francisco García (5)", "Francisco García (5)", "ARCO Arena\\n11,168", "9–30"], ["47", "January 28", "@ Boston", "L 100–119", "John Salmons (22)", "Jason Thompson (11)", "John Salmons (5)", "TD Banknorth Garden\\n18,624", "10–37"], ["45", "January 25", "@ Toronto", "L 97–113", "John Salmons (21)", "John Salmons (7)", "Beno Udrih (5)", "Air Canada Centre\\n18,127", "10–35"]]	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 point difference between the sacramento and detroit for game 33?	6	128	Answer:
Table InputTable: [["Name", "Location", "Date established", "Area", "Description"], ["American Samoa", "American Samoa\\n14°15′S 170°41′W / 14.25°S 170.68°W", "October 31, 1988", "9,000.00 acres (36.4 km2)", "The southernmost national park is on three Samoan islands and protects coral reefs, rainforests, volcanic mountains, and white beaches. The area is also home to flying foxes, brown boobies, sea turtles, and 900 species of fish."], ["Acadia", "Maine\\n44°21′N 68°13′W / 44.35°N 68.21°W", "February 26, 1919", "47,389.67 acres (191.8 km2)", "Covering most of Mount Desert Island and other coastal islands, Acadia features the tallest mountain on the Atlantic coast of the United States, granite peaks, ocean shoreline, woodlands, and lakes. There are freshwater, estuary, forest, and intertidal habitats."], ["Virgin Islands", "United States Virgin Islands\\n18°20′N 64°44′W / 18.33°N 64.73°W", "August 2, 1956", "14,688.87 acres (59.4 km2)", "The island of Saint John has rich human and natural history. There are Taino archaeological sites and ruins of sugar plantations from Columbus's time. Past the pristine beaches are mangroves, seagrass beds, coral reefs and algal plains."], ["Voyageurs", "Minnesota\\n48°30′N 92°53′W / 48.50°N 92.88°W", "January 8, 1971", "218,200.17 acres (883.0 km2)", "This park on four main lakes, a site for canoeing, kayaking, and fishing, has a history of Ojibwe Native Americans, French fur traders called voyageurs, and a gold rush. Formed by glaciers, this region has tall bluffs, rock gardens, islands and bays, and historic buildings."], ["Olympic", "Washington\\n47°58′N 123°30′W / 47.97°N 123.50°W", "June 29, 1938", "922,650.86 acres (3,733.8 km2)", "Situated on the Olympic Peninsula, this park ranges from Pacific shoreline with tide pools to temperate rainforests to Mount Olympus. The glaciated Olympic Mountains overlook the Hoh Rain Forest and Quinault Rain Forest, the wettest area of the continental United States."], ["Everglades", "Florida\\n25°19′N 80°56′W / 25.32°N 80.93°W", "May 30, 1934", "1,508,537.90 acres (6,104.8 km2)", "The Everglades are the largest subtropical wilderness in the United States. This mangrove ecosystem and marine estuary is home to 36 protected species, including the Florida panther, American crocodile, and West Indian manatee. Some areas have been drained and developed; restoration projects aim to restore the ecology."], ["Denali", "Alaska\\n63°20′N 150°30′W / 63.33°N 150.50°W", "February 26, 1917", "4,740,911.72 acres (19,185.8 km2)", "Centered around the Mount McKinley, the tallest mountain in North America, Denali is serviced by a single road leading to Wonder Lake. McKinley and other peaks of the Alaska Range are covered with long glaciers and boreal forest. Wildlife includes grizzly bears, Dall sheep, caribou, and gray wolves."], ["Dry Tortugas", "Florida\\n24°38′N 82°52′W / 24.63°N 82.87°W", "October 26, 1992", "64,701.22 acres (261.8 km2)", "The Dry Tortugas on the west end of the Florida Keys are the site of Fort Jefferson, the largest masonry structure in the Western Hemisphere. With most of the park being water, it is the home of coral reefs and shipwrecks and is only accessible by plane or boat."], ["Lake Clark", "Alaska\\n60°58′N 153°25′W / 60.97°N 153.42°W", "December 2, 1980", "2,619,733.21 acres (10,601.7 km2)", "The region around Lake Clark has four active volcanoes, including Mount Redoubt, rivers, glaciers, and waterfalls. There are temperate rainforests, a tundra plateau, and three mountain ranges."]]	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:was acadia or american samoa established first?	Acadia	128	Answer:
Table InputTable: [["Season", "Team", "Record", "Head Coach", "Quarterback", "Leading Rusher", "Leading Receiver", "All-Pros", "Runner Up"], ["1975", "Dallas Cowboys", "10–4", "Tom Landry", "Roger Staubach", "Robert Newhouse", "Drew Pearson", "none", "Los Angeles Rams"], ["1977", "Dallas Cowboys†", "12–2", "Tom Landry", "Roger Staubach", "Tony Dorsett", "Drew Pearson", "Harris, Herrera, Martin, Pearson", "Minnesota Vikings"], ["1973", "Minnesota Vikings", "12–2", "Bud Grant", "Fran Tarkenton", "Chuck Foreman", "John Gilliam", "Eller, Page, Yary", "Dallas Cowboys"], ["1978", "Dallas Cowboys", "12–4", "Tom Landry", "Roger Staubach", "Tony Dorsett", "Tony Hill", "Harris, White", "Los Angeles Rams"], ["1981", "San Francisco 49ers†", "13–3", "Bill Walsh", "Joe Montana", "Ricky Patton", "Dwight Clark", "Dean, Lott", "Dallas Cowboys"], ["1971", "Dallas Cowboys†", "11–3", "Tom Landry", "Roger Staubach", "Duane Thomas", "Bob Hayes", "Lilly, Niland, Wright", "San Francisco 49ers"], ["1972", "Washington Redskins", "11–3", "George Allen", "Billy Kilmer", "Larry Brown", "Charley Taylor", "Brown, Hanburger", "Dallas Cowboys"], ["1970", "Dallas Cowboys", "10–4", "Tom Landry", "Craig Morton", "Duane Thomas", "Bob Hayes", "Howley", "San Francisco 49ers"], ["1974", "Minnesota Vikings", "10–4", "Bud Grant", "Fran Tarkenton", "Chuck Foreman", "Jim Lash", "Page, Yary", "Los Angeles Rams"], ["2001", "St. Louis Rams", "14–2", "Mike Martz", "Kurt Warner", "Marshall Faulk", "Torry Holt", "Faulk, Pace, Warner, Williams", "Philadelphia Eagles"], ["1995", "Dallas Cowboys†", "12–4", "Barry Switzer", "Troy Aikman", "Emmitt Smith", "Michael Irvin", "Newton, Smith, Woodson", "Green Bay Packers"], ["1976", "Minnesota Vikings", "11–2–1", "Bud Grant", "Fran Tarkenton", "Chuck Foreman", "Sammy White", "Yary", "Los Angeles Rams"], ["1993", "Dallas Cowboys†", "12–4", "Jimmy Johnson", "Troy Aikman", "Emmitt Smith", "Michael Irvin", "Smith, Williams", "San Francisco 49ers"], ["1980", "Philadelphia Eagles", "12–4", "Dick Vermeil", "Ron Jaworski", "Wilbert Montgomery", "Charlie Smith", "Johnson", "Dallas Cowboys"], ["1998", "Atlanta Falcons", "14–2", "Dan Reeves", "Chris Chandler", "Jamal Anderson", "Tony Martin", "Anderson", "Minnesota Vikings"], ["2002", "Tampa Bay Buccaneers†", "12–4", "Jon Gruden", "Brad Johnson", "Michael Pittman", "Keyshawn Johnson", "Brooks, Rice, Sapp", "Philadelphia Eagles"], ["2004", "Philadelphia Eagles", "13–3", "Andy Reid", "Donovan McNabb", "Brian Westbrook", "Terrell Owens", "Dawkins, Owens, Sheppard", "Atlanta Falcons"], ["1987", "Washington Redskins†", "11–4", "Joe Gibbs", "Jay Schroeder", "George Rogers", "Gary Clark", "Clark, Wilburn", "Minnesota Vikings"], ["1992", "Dallas Cowboys†", "13–3", "Jimmy Johnson", "Troy Aikman", "Emmitt Smith", "Michael Irvin", "Novacek, Smith", "San Francisco 49ers"], ["2003", "Carolina Panthers", "11–5", "John Fox", "Jake Delhomme", "Stephen Davis", "Steve Smith", "Jenkins", "Philadelphia Eagles"], ["1982", "Washington Redskins†", "8–1", "Joe Gibbs", "Joe Theismann", "John Riggins", "Charlie Brown", "Moseley", "Dallas Cowboys"], ["1994", "San Francisco 49ers†", "13–3", "George Seifert", "Steve Young", "Ricky Watters", "Jerry Rice", "Rice, Sanders, Young", "Dallas Cowboys"]]	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 won the nfc championship after the dallas cowboys in 1975?	Minnesota Vikings	128	Answer:
Table InputTable: [["Rank", "City", "Passengers", "Ranking", "Airline"], ["3", "Guerrero, Acapulco", "56,069", "", "Aeroméxico Connect, Interjet"], ["2", "Nuevo León, Monterrey", "106,513", "", "Aeroméxico Connect, Interjet"], ["6", "Baja California Sur, Los Cabos", "37,526", "1", "Interjet"], ["5", "Jalisco, Puerto Vallarta", "43,419", "1", "Interjet"], ["1", "Quintana Roo, Cancún", "132,046", "", "Aeroméxico Connect, Interjet, Volaris"], ["7", "Guerrero, Ixtapa/Zihuatanejo", "35,507", "", "Interjet"], ["8", "Baja California, Tijuana", "14,906", "", "Interjet"], ["4", "Jalisco, Guadalajara", "52,584", "", "Aeroméxico Connect, Volaris"], ["10", "Tamaulipas, Tampico", "3,619", "1", "VivaAerobus"], ["9", "Tabasco, Villahermosa", "6,928", "1", "VivaAerobus"]]	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 were the total number of routes that used interjet?	7	128	Answer:
Table InputTable: [["Episode", "Show #", "Iron Chef", "Challenger", "Challenger specialty", "Secret ingredient(s) or theme", "Winner", "Final score"], ["5", "IA0504", "Cat Cora", "Mark Tarbell", "Seasonal Organic", "Apples", "Mark Tarbell", "50-44"], ["11", "IA0503", "Cat Cora", "Todd Richards", "Modern Southern", "Carrots", "Cat Cora", "48-46"], ["8", "IA0507", "Cat Cora", "Mary Dumont", "French-American", "Milk and cream", "Cat Cora", "51-46"], ["3", "IA0509", "Cat Cora", "Alexandra Guarnaschelli", "French-American", "Farmers' Market", "Cat Cora", "45-41"], ["9", "IASP07", "Michael Symon", "Ricky Moore", "Contemporary American", "Traditional Thanksgiving", "Michael Symon", "51-43"], ["10", "IASP08", "Cat Cora & Paula Deen", "Tyler Florence & Robert Irvine", "Southern (Deen), Contemporary American (Florence), International (Irvine)", "Sugar", "Cat Cora & Paula Deen", "49-47"], ["7", "IA0510", "Mario Batali", "Charles Clark", "New American", "Halibut", "Mario Batali", "51-50"], ["2", "IA0508", "Mario Batali", "Tony Liu", "Pan-European", "Opah", "Mario Batali", "55-47"], ["6", "IA0506", "Bobby Flay", "Kurt Boucher", "French-American", "Arctic char", "Bobby Flay", "46-39"], ["1", "IA0502", "Bobby Flay", "Ben Ford", "Regional American", "Blue foot chicken", "Bobby Flay", "44-35"], ["4", "IA0501", "Mario Batali", "Andrew Carmellini", "Urban Italian", "Parmigiano-Reggiano", "Mario Batali", "56-55"], ["12", "IA0505", "Masaharu Morimoto", "Fortunato Nicotra", "Seasonal Italian", "Kampachi", "Masaharu Morimoto", "59-50"]]	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 secret ingredient after sugar?	Carrots	128	Answer:
Table InputTable: [["Season", "Competition", "Round", "Club", "Home", "Away"], ["1986–87", "UEFA Cup", "1R", "FC Barcelona", "1–1", "0–0"], ["1987–88", "UEFA Cup", "1/16", "FC Barcelona", "1–0", "1–4"], ["1987–88", "UEFA Cup", "1R", "FK Partizan Beograd", "2–0", "1–2"], ["", "", "2QR", "FK Jablonec 97", "0–2", "1–5"], ["1985–86", "UEFA Cup Winners' Cup", "1R", "HJK Helsinki", "1–2", "2–3"], ["1987–88", "UEFA Cup", "2R", "Wismut Aue", "2–0", "0–1"], ["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"], ["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"], ["1991–92", "UEFA European Cup", "1R", "IFK Göteborg", "1–1", "0–0"], ["2012–13", "UEFA Europa League", "1QR", "Budapest Honvéd", "0–1", "0–2"]]	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:only club shown twice	FC Barcelona	128	Answer:
Table InputTable: [["Season", "Winning Team", "Score", "Losing Team", "Score", "Location", "Stadium"], ["1994–95", "San Diego Chargers (1)", "17", "Pittsburgh Steelers", "13", "Pittsburgh, Pennsylvania", "Three Rivers Stadium"], ["1979–80", "Pittsburgh Steelers (4)", "27", "Houston Oilers", "13", "Pittsburgh, Pennsylvania", "Three Rivers Stadium"], ["1978–79", "Pittsburgh Steelers (3)", "34", "Houston Oilers", "5", "Pittsburgh, Pennsylvania", "Three Rivers Stadium"], ["1972–73", "Miami Dolphins (2)", "21", "Pittsburgh Steelers", "17", "Pittsburgh, Pennsylvania", "Three Rivers Stadium"], ["1997–98", "Denver Broncos (5)", "24", "Pittsburgh Steelers", "21", "Pittsburgh, Pennsylvania", "Three Rivers Stadium"], ["1975–76", "Pittsburgh Steelers (2)", "16", "Oakland Raiders", "10", "Pittsburgh, Pennsylvania", "Three Rivers Stadium"], ["1995–96", "Pittsburgh Steelers (5)", "20", "Indianapolis Colts", "16", "Pittsburgh, Pennsylvania", "Three Rivers Stadium"], ["1981–82", "Cincinnati Bengals (1)", "27", "San Diego Chargers", "7", "Cincinnati, Ohio", "Riverfront Stadium"], ["1980–81", "Oakland Raiders (2)", "34", "San Diego Chargers", "27", "San Diego, California", "Jack Murphy Stadium"], ["1990–91", "Buffalo Bills (1)", "51", "Los Angeles Raiders", "3", "Orchard Park, New York", "Ralph Wilson Stadium"], ["1992–93", "Buffalo Bills (3)", "29", "Miami Dolphins", "10", "Miami, Florida", "Joe Robbie Stadium"], ["1993–94", "Buffalo Bills (4)", "30", "Kansas City Chiefs", "13", "Orchard Park, New York", "Ralph Wilson Stadium"], ["2007–08", "New England Patriots (6)", "21", "San Diego Chargers", "12", "Foxborough, Massachusetts", "Gillette Stadium"], ["2009–10", "Indianapolis Colts (3)", "30", "New York Jets", "17", "Indianapolis, Indiana", "Lucas Oil Stadium"], ["1987–88", "Denver Broncos (3)", "38", "Cleveland Browns", "33", "Denver, Colorado", "Mile High Stadium"], ["1988–89", "Cincinnati Bengals (2)", "21", "Buffalo Bills", "10", "Cincinnati, Ohio", "Riverfront Stadium"], ["1998–99", "Denver Broncos (6)", "23", "New York Jets", "10", "Denver, Colorado", "Mile High Stadium"], ["1989–90", "Denver Broncos (4)", "37", "Cleveland Browns", "21", "Denver, Colorado", "Mile High Stadium"], ["1970–71", "Baltimore Colts (1)", "27", "Oakland Raiders", "17", "Baltimore, Maryland", "Memorial Stadium"], ["2000–01", "Baltimore Ravens (1)", "16", "Oakland Raiders", "3", "Oakland, California", "Oakland Coliseum"], ["2003–04", "New England Patriots (4)", "24", "Indianapolis Colts", "14", "Foxborough, Massachusetts", "Gillette Stadium"], ["2001–02", "New England Patriots (3)", "24", "Pittsburgh Steelers", "17", "Pittsburgh, Pennsylvania", "Heinz Field"], ["1973–74", "Miami Dolphins (3)", "27", "Oakland Raiders", "10", "Miami, Florida", "Miami Orange Bowl"], ["1999–00", "Tennessee Titans (1)", "33", "Jacksonville Jaguars", "14", "Jacksonville, Florida", "Jacksonville Municipal Stadium"], ["2008–09", "Pittsburgh Steelers (7)", "23", "Baltimore Ravens", "14", "Pittsburgh, Pennsylvania", "Heinz Field"], ["2005–06", "Pittsburgh Steelers (6)", "34", "Denver Broncos", "17", "Denver, Colorado", "Invesco Field at Mile High"], ["1991–92", "Buffalo Bills (2)", "10", "Denver Broncos", "7", "Orchard Park, New York", "Ralph Wilson Stadium"], ["1977–78", "Denver Broncos (1)", "20", "Oakland Raiders", "17", "Denver, Colorado", "Mile High Stadium"]]	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 number of times did a game take place in the three rivers stadium?	7	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"], ["6", "22 Apr 2007", "Tsuruya Open", "−16 (67-65-68-68=268)", "2 strokes", "Masahiro Kuramoto, Hirofumi Miyase,\\n Takuya Taniguchi"], ["8", "2 Dec 2007", "Golf Nippon Series JT Cup", "−11 (70-70-68-61=261)", "1 stroke", "Toru Taniguchi"], ["1", "3 Nov 2002", "Philip Morris K.K. Championship", "−19 (65-67-67-70=269)", "2 strokes", "Toshimitsu Izawa"], ["11", "15 Apr 2012", "Token Homemate Cup", "−15 (68-69-70-62=269)", "2 strokes", "Ryuichi Oda"], ["5", "23 Apr 2006", "Tsuruya Open", "−11 (70-68-66-69=273)", "2 strokes", "Mamo Osanai"], ["7", "11 Nov 2007", "Mitsui Sumitomo VISA Taiheiyo Masters", "−13 (67-68-69-70=274)", "5 strokes", "Toru Taniguchi"], ["9", "26 Sep 2010", "Asia-Pacific Panasonic Open\\n(co-sanctioned by the Asian Tour)", "−6 (71-70-66=207)", "1 stroke", "Ryuichi Oda"], ["2", "10 Aug 2003", "Sun Chlorella Classic", "−8 (71-73-68-68=280)", "1 stroke", "Daisuke Maruyama, Taichi Teshima"], ["13", "30 Jun 2013", "Gateway to the Open Mizuno Open", "−19 (67-66-68-68=269)", "3 strokes", "Kim Kyung-tae"], ["12", "29 Jul 2012", "Sun Chlorella Classic", "−15 (69-66-68-70=273)", "2 strokes", "Lee Seong-ho, Hideki Matsuyama (am),\\n Yoshinobu Tsukada"], ["4", "26 Jun 2004", "Gateway to the Open Mizuno Open", "−14 (67-68-70-69=274)", "Playoff", "Hiroaki Iijima"], ["10", "1 May 2011", "The Crowns", "−9 (67-66-68-70=271)", "Playoff", "Jang Ik-jae"]]	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 brendan jones' runner(s) up on december 2, 2007?	Toru Taniguchi	128	Answer:
Table InputTable: [["Round", "Date", "Circuit", "Winning driver (TA2)", "Winning vehicle (TA2)", "Winning driver (TA1)", "Winning vehicle (TA1)"], ["3", "June 11", "Portland", "Tuck Thomas", "Chevrolet Monza", "Bob Matkowitch", "Chevrolet Corvette"], ["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"], ["9", "October 8", "Laguna Seca", "Greg Pickett", "Chevrolet Corvette", "Bob Tullius", "Jaguar XJS"], ["8", "September 4", "Road America", "Greg Pickett", "Chevrolet Corvette", "Bob Tullius", "Jaguar XJS"], ["2", "June 4", "Westwood", "Ludwig Heimrath", "Porsche 935", "Nick Engels", "Chevrolet Corvette"], ["1", "May 21", "Sears Point", "Greg Pickett", "Chevrolet Corvette", "Gene Bothello", "Chevrolet Corvette"], ["4", "June 25", "Mont-Tremblant", "Monte Sheldon", "Porsche 935", "Bob Tullius", "Jaguar XJS"], ["6", "August 13", "Brainerd", "Jerry Hansen", "Chevrolet Monza", "Bob Tullius", "Jaguar XJS"], ["10", "November 5", "Mexico City", "Ludwig Heimrath", "Porsche 935", "Bob Tullius", "Jaguar XJS"]]	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 next winning driver(t a2)listed after tuck thomas?	Monte Sheldon	128	Answer:
Table InputTable: [["Competition", "Total spectatorship", "Average match attendance", "Year"], ["Women's National Basketball League", "77,944", "", "2010/2011"], ["National Basketball League", "547,021", "4,031", "2010/2011"], ["A-League", "1,772,133", "12,707", "2012/2013"], ["Big Bash League", "550,262", "17,750", "2011/2012"], ["Australian Football League", "6,931,085", "33,484", "2013"], ["Rugby Championship", "133,532", "44,511", "2012"], ["National Rugby League", "3,345,248", "16,643", "2013"], ["Super Rugby", "773,940", "19,348", "2012"], ["State of Origin series", "186,607", "62,202", "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:excluding the women's national basketball league, what competition had the lowest average match attendance?	National Basketball League	128	Answer:
Table InputTable: [["Pos", "No", "Driver", "Constructor", "Laps", "Time/Retired", "Grid", "Points"], ["6", "24", "Rolf Stommelen", "Surtees-Ford", "79", "+ 1 Lap", "16", "1"], ["10", "8", "Tim Schenken", "Brabham-Ford", "76", "+ 4 Laps", "18", ""], ["DNQ", "6", "Mario Andretti", "Ferrari", "", "", "", ""], ["8", "27", "Henri Pescarolo", "March-Ford", "77", "+ 3 Laps", "13", ""], ["Ret", "7", "Graham Hill", "Brabham-Ford", "1", "Accident", "9", ""], ["7", "22", "John Surtees", "Surtees-Ford", "79", "+ 1 Lap", "10", ""], ["Ret", "10", "Peter Gethin", "McLaren-Ford", "22", "Accident", "14", ""], ["9", "15", "Pedro Rodríguez", "BRM", "76", "+ 4 Laps", "5", ""], ["5", "1", "Emerson Fittipaldi", "Lotus-Ford", "79", "+ 1 Lap", "17", "2"], ["Ret", "5", "Clay Regazzoni", "Ferrari", "24", "Accident", "11", ""], ["4", "9", "Denny Hulme", "McLaren-Ford", "80", "+ 1:06.7", "8", "3"], ["DNQ", "19", "Nanni Galli*", "March-Alfa-Romeo", "", "", "", ""], ["Ret", "2", "Reine Wisell", "Lotus-Ford", "21", "Wheel bearing", "12", ""], ["Ret", "12", "François Cevert", "Tyrrell-Ford", "5", "Accident", "15", ""], ["DNQ", "18", "Alex Soler-Roig", "March-Ford", "", "", "", ""], ["1", "11", "Jackie Stewart", "Tyrrell-Ford", "80", "1:52:21.3", "1", "9"], ["3", "4", "Jacky Ickx", "Ferrari", "80", "+ 53.3", "2", "4"], ["Ret", "14", "Jo Siffert", "BRM", "58", "Oil Pipe", "3", ""], ["DNQ", "28", "Skip Barber", "March-Ford", "", "", "", ""], ["Ret", "20", "Chris Amon", "Matra", "45", "Differential", "4", ""], ["2", "17", "Ronnie Peterson", "March-Ford", "80", "+ 25.6", "6", "6"], ["Ret", "21", "Jean-Pierre Beltoise", "Matra", "47", "Differential", "7", ""], ["DNQ", "16", "Howden Ganley", "BRM", "", "", "", ""]]	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:tell me the first driver from germany to finish.	Rolf Stommelen	128	Answer:
Table InputTable: [["Year", "Single", "Chart Positions\\nUS Country", "Chart Positions\\nUS", "Album"], ["1964", "\"Blue Train (Of the Heartbreak Line)\"", "44", "132", "singles only"], ["1963", "\"Bad News\" (b/w \"Guitar Player(Her and Him)\")", "23", "—", "singles only"], ["1965", "\"That Ain't All\"", "20", "—", "singles only"], ["1967", "\"It's My Time\"", "51", "—", "Suburban Attitudes in Country Verse"], ["1962", "\"Callin' Dr. Casey\"", "—", "83", "singles only"], ["1964", "\"Th' Wife\"", "45", "—", "singles only"], ["1962", "\"Thou Shalt Not Steal\"", "—", "73", "singles only"], ["1966", "\"You're the Guilty One\"", "—", "—", "single only"], ["1968", "\"Odd Folks of Okracoke\"", "—", "—", "single only"], ["1962", "\"Road Hog\"", "—", "65", "Twelve Sides"], ["1957", "\"Sittin' in the Balcony\"", "—", "38", "single only"], ["1961", "\"Language of Love\"", "—", "32", "Language of Love"], ["1971", "\"Lord Have Mercy\"", "—", "—", "Volume 1-Elloree"], ["1969", "\"Brown Girl\"", "—", "—", "The Open Mind of John D. Loudermilk"], ["1966", "\"Silver Cloud Talkin' Blues\"", "—", "—", "A Bizarre Collection of the Most Unusual Songs"], ["1979", "\"Every Day I Learn a Little More About Love\"", "—", "—", "Just Passing Through"]]	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 us country chart positions were less than 40?	2	128	Answer:
Table InputTable: [["Rank", "Player", "County", "Tally", "Total", "Opposition"], ["9", "John Leahy", "Tipperary", "2–2", "8", "Kerry"], ["3", "Gary Kirby", "Limerick", "0–10", "10", "Tipperary"], ["6", "David Martin", "Meath", "1–6", "9", "Offaly"], ["6", "Gary Kirby", "Limerick", "0–9", "9", "Antrim"], ["9", "John Byrne", "Carlow", "2–2", "8", "Westmeath"], ["6", "Seán McLoughlin", "Westmeath", "1–6", "9", "Carlow"], ["3", "Gary Kirby", "Limerick", "1–7", "10", "Tipperary"], ["2", "Niall English", "Carlow", "1–9", "12", "Westmeath"], ["9", "Paul Flynn", "Waterford", "1–5", "8", "Tipperary"], ["9", "John Troy", "Offaly", "2–2", "8", "Laois"], ["9", "Tom Dempsey", "Wexford", "1–5", "8", "Offaly"], ["3", "Kevin Broderick", "Galway", "3–1", "10", "New York"], ["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:what number of players had a tally of 2-2?	3	128	Answer:
Table InputTable: [["Name", "Pennant", "Namesake", "Builder", "Ordered", "Laid down", "Launched", "Commissioned", "Fate"], ["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"], ["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"], ["Ajax", "22", "Ajax the Great", "Vickers Armstrong", "1 October 1932", "7 February 1933", "1 March 1934", "12 April 1935", "Broken up at Newport, 1949"], ["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"], ["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"], ["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"], ["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 builder comes after vickers armstrong in the chart?	Cammell Laird	128	Answer:
Table InputTable: [["Number", "Name", "Term Started", "Term Ended", "Alma Mater", "Field(s)", "Educational Background"], ["8", "Major General Raza Hussain", "2001", "2010", "Pakistan Army Corps of Electrical and Mechanical Engineers", "Electrical Engineering", "B.S."], ["9", "Major General Ahmed Bilal", "2010", "Present", "Pakistan Army Corps of Signals Engineering", "Computer Engineering", "Master of Science (M.S)"], ["3", "Air Commodore K. M. Ahmad", "1979", "1980", "Pakistan Air Force Academy", "Flight Instructor", "Certificated Flight Instructor (CFI)"], ["5", "Dr M. Shafi Ahmad", "1989", "1990", "University of London", "Astronomy", "Ph.D"], ["1", "Dr Abdus Salam", "1961", "1967", "Imperial College", "Theoretical Physics", "Doctor of Philosophy (Ph.D)"], ["7", "Dr Abdul Majid", "1997", "2001", "University of Wales", "Astrophysics", "Ph.D"], ["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"], ["6", "Engr.Sikandar Zaman", "1990", "1997", "University of Leeds", "Mechanical Engineering", "Bachelor of Science (B.S.)"], ["2", "Air Commodore Dr Władysław Turowicz", "1967", "1979", "Warsaw University of Technology", "Aeronautical 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:what suparco administrator has the same field as major general raza hussain?	Dr Salim Mehmud	128	Answer:
Table InputTable: [["#", "Name", "Took office", "Left office", "Party", "Governor", "Notes"], ["46", "Frank O'Bannon", "January 9, 1989", "January 13, 1997", "Democrat", "Evan Bayh", ""], ["44", "Robert D. Orr", "January 8, 1973", "January 12, 1981", "Republican", "Otis R. Bowen", ""], ["48", "Kathy Davis", "October 20, 2003", "January 10, 2005", "Democrat", "Joe E. Kernan", ""], ["45", "John Mutz", "January 12, 1981", "January 9, 1989", "Republican", "Robert D. Orr", ""], ["21", "Robert S. Robertson", "January 10, 1887", "January 13, 1889", "Republican", "Isaac P. Gray", ""], ["50", "Sue Ellspermann", "January 14, 2013", "Incumbent", "Republican", "Mike Pence", ""], ["22", "Ira Joy Chase", "January 14, 1889", "November 24, 1891", "Republican", "Alvin Peterson Hovey", "acting"], ["19", "Thomas Hanna", "January 10, 1881", "November 12, 1885", "Republican", "Albert G. Porter", ""], ["41", "Richard O. Ristine", "January 9, 1961", "January 11, 1965", "Republican", "Matthew E. Welsh", ""], ["31", "F. Harold Van Orman", "January 12, 1925", "January 14, 1929", "Republican", "Edward L. Jackson", ""], ["49", "Becky Skillman", "January 10, 2005", "January 14, 2013", "Republican", "Mitch Daniels", ""], ["–", "Alonzo G. Smith", "November 8, 1886", "January 14, 1889", "Democrat", "Isaac P. Gray", "acting"], ["47", "Joe E. Kernan", "January 13, 1997", "September 13, 2003", "Democrat", "Frank O'Bannon", ""], ["42", "Robert L. Rock", "January 11, 1965", "January 13, 1969", "Democrat", "Roger D. Branigin", ""], ["36", "Richard T. James", "January 8, 1945", "January 10, 1948", "Republican", "Ralph F. Gates", ""], ["5", "Milton Stapp", "December 3, 1828", "December 7, 1831", "Independent", "James B. Ray", ""], ["43", "Richard E. Folz", "January 13, 1969", "January 8, 1973", "Republican", "Edgar Whitcomb", ""], ["14", "Oliver P. Morton", "January 14, 1861", "January 16, 1861", "Republican", "Henry Smith Lane", ""], ["39", "Harold W. Handley", "January 12, 1953", "January 14, 1957", "Republican", "George N. Craig", ""], ["40", "Crawford F. Parker", "January 14, 1957", "January 9, 1961", "Republican", "Harold W. Handley", ""], ["29", "Edgar D. Bush", "January 8, 1917", "January 10, 1921", "Republican", "James P. Goodrich", ""], ["4", "John H. Thompson", "January 30, 1824", "December 3, 1828", "Democratic-Republican", "William Hendricks", ""], ["9", "Jesse D. Bright", "December 6, 1843", "December 6, 1845", "Democrat", "James Whitcomb", ""], ["8", "Samuel Hall", "December 9, 1840", "December 6, 1843", "Whig", "Samuel Bigger", ""], ["32", "Edgar D. Bush", "January 14, 1929", "January 9, 1933", "Republican", "Harry G. Leslie", ""], ["6", "David Wallace", "December 7, 1831", "December 6, 1837", "Whig", "Noah Noble", ""]]	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 did evan bayh serve as indiana's governor?	8	128	Answer:
Table InputTable: [["", "Name on the Register", "Date listed", "Location", "City or town", "Summary"], ["16", "New Durham Meetinghouse and Pound", "December 8, 1980\\n(#80000312)", "Old Bay Rd.\\n43°25′25″N 71°07′42″W / 43.423611°N 71.128333°W", "New Durham", ""], ["27", "Salmon Falls Mill Historic District", "February 29, 1980\\n(#80000315)", "Front St.\\n43°14′10″N 70°49′05″W / 43.236111°N 70.818056°W", "Rollinsford", ""], ["23", "Religious Society of Friends Meetinghouse", "February 29, 1980\\n(#80000421)", "141 Central Ave.\\n43°11′12″N 70°52′25″W / 43.186667°N 70.873611°W", "Dover", ""], ["40", "Samuel Wyatt House", "December 2, 1982\\n(#82000626)", "7 Church St.\\n43°11′30″N 70°52′31″W / 43.191667°N 70.875278°W", "Dover", ""], ["22", "Michael Reade House", "February 12, 1980\\n(#80000314)", "43 Main St.\\n43°11′50″N 70°52′21″W / 43.197222°N 70.8725°W", "Dover", ""], ["8", "Free Will Baptist Church", "November 13, 1980\\n(#80000310)", "Ridge Top Road\\n43°23′59″N 71°09′33″W / 43.399722°N 71.159167°W", "New Durham", ""], ["14", "Lehoullier Building", "December 26, 1979\\n(#79000211)", "161-169 Main St.\\n43°15′31″N 70°51′46″W / 43.258611°N 70.862778°W", "Somersworth", ""], ["4", "Durham Historic District", "May 31, 1980\\n(#80000308)", "Main St. and Newmarket Rd.\\n43°07′47″N 70°55′10″W / 43.129722°N 70.919444°W", "Durham", ""], ["11", "William Hale House", "November 18, 1980\\n(#80000309)", "5 Hale St.\\n43°11′36″N 70°52′29″W / 43.193376°N 70.874858°W", "Dover", ""], ["28", "Sawyer Building", "May 23, 1980\\n(#80000316)", "4-6 Portland St.\\n43°11′48″N 70°52′21″W / 43.196667°N 70.8725°W", "Dover", ""], ["33", "Gen. John Sullivan House", "November 28, 1972\\n(#72000089)", "23 Newmarket Rd.\\n43°07′48″N 70°55′05″W / 43.13°N 70.918056°W", "Durham", "Home of American Revolutionary War General John Sullivan, elected President of New Hampshire."], ["17", "New Durham Town Hall", "November 13, 1980\\n(#80000313)", "Main St. and Ridge Rd.\\n43°26′02″N 71°09′55″W / 43.433889°N 71.165278°W", "New Durham", ""], ["6", "First Parish Church", "March 11, 1982\\n(#82001696)", "218 Central Ave.\\n43°10′56″N 70°52′27″W / 43.182222°N 70.874167°W", "Dover", ""], ["15", "Milton Town House", "November 26, 1980\\n(#80000311)", "NH 125 and Town House Rd.\\n43°26′27″N 70°59′05″W / 43.440833°N 70.984722°W", "Milton", ""], ["39", "Woodman Institute", "July 24, 1980\\n(#80000317)", "182 Central Ave.\\n43°11′20″N 70°52′28″W / 43.188889°N 70.874444°W", "Dover", ""]]	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 historic places were listed in the 1980's?	29	128	Answer:
Table InputTable: [["Pos", "Rider", "Manufacturer", "Time/Retired", "Points"], ["1", "Loris Capirossi", "Honda", "38:04.730", "25"], ["2", "Valentino Rossi", "Aprilia", "+0.180", "20"], ["22", "Rudie Markink", "Aprilia", "+1:40.280", ""], ["7", "Franco Battaini", "Aprilia", "+20.889", "9"], ["12", "Sebastian Porto", "Yamaha", "+27.054", "4"], ["3", "Jeremy McWilliams", "Aprilia", "+0.534", "16"], ["20", "Lucas Oliver Bulto", "Yamaha", "+1:25.758", ""], ["13", "Tomomi Manako", "Yamaha", "+27.903", "3"], ["24", "Henk Van De Lagemaat", "Honda", "+1 Lap", ""], ["16", "Luca Boscoscuro", "TSR-Honda", "+56.432", ""], ["23", "Arno Visscher", "Aprilia", "+1:40.635", ""], ["8", "Stefano Perugini", "Honda", "+20.891", "8"], ["19", "Fonsi Nieto", "Yamaha", "+1:25.622", ""], ["15", "Jarno Janssen", "TSR-Honda", "+56.248", "1"], ["21", "David Garcia", "Yamaha", "+1:33.867", ""], ["6", "Ralf Waldmann", "Aprilia", "+7.019", "10"], ["14", "Masaki Tokudome", "TSR-Honda", "+33.161", "2"], ["10", "Anthony West", "TSR-Honda", "+26.816", "6"], ["17", "Johann Stigefelt", "Yamaha", "+1:07.433", ""], ["Ret", "Roberto Rolfo", "Aprilia", "Retirement", ""], ["5", "Shinya Nakano", "Yamaha", "+0.742", "11"], ["11", "Alex Hofmann", "TSR-Honda", "+26.933", "5"], ["Ret", "Marcellino Lucchi", "Aprilia", "Retirement", ""], ["18", "Julien Allemand", "TSR-Honda", "+1:16.347", ""], ["4", "Tohru Ukawa", "Honda", "+0.537", "13"], ["Ret", "Maurice Bolwerk", "TSR-Honda", "Retirement", ""], ["9", "Jason Vincent", "Honda", "+21.310", "7"], ["Ret", "Andre Romein", "Honda", "Retirement", ""]]	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 points that loris capirossi finished with?	25	128	Answer:
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["2", "United States (USA)", "2", "1", "1", "4"], ["5", "Soviet Union (URS)", "0", "1", "2", "3"], ["", "Total", "8", "8", "8", "24"], ["3", "West Germany (FRG)", "2", "0", "0", "2"], ["6", "Sweden (SWE)", "0", "1", "1", "2"], ["1", "Netherlands (NED)", "4", "3", "2", "9"], ["4", "Norway (NOR)", "0", "2", "2", "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 number of medals that the united states had?	4	128	Answer:
Table InputTable: [["Atomic\\nno.", "Name", "Symbol", "Group", "Period", "Block", "State at\\nSTP", "Occurrence", "Description"], ["53", "Iodine", "I", "17", "5", "p", "Solid", "Primordial", "Halogen"], ["102", "Nobelium", "No", "3", "7", "f", "Solid", "Synthetic", "Actinide"], ["99", "Einsteinium", "Es", "3", "7", "f", "Solid", "Synthetic", "Actinide"], ["90", "Thorium", "Th", "3", "7", "f", "Solid", "Primordial", "Actinide"], ["34", "Selenium", "Se", "16", "4", "p", "Solid", "Primordial", "Non-metal"], ["97", "Berkelium", "Bk", "3", "7", "f", "Solid", "Transient", "Actinide"], ["35", "Bromine", "Br", "17", "4", "p", "Liquid", "Primordial", "Halogen"], ["91", "Protactinium", "Pa", "3", "7", "f", "Solid", "Transient", "Actinide"], ["95", "Americium", "Am", "3", "7", "f", "Solid", "Transient", "Actinide"], ["103", "Lawrencium", "Lr", "3", "7", "d", "Solid", "Synthetic", "Actinide"], ["96", "Curium", "Cm", "3", "7", "f", "Solid", "Transient", "Actinide"], ["33", "Arsenic", "As", "15", "4", "p", "Solid", "Primordial", "Metalloid"], ["38", "Strontium", "Sr", "2", "5", "s", "Solid", "Primordial", "Alkaline earth metal"], ["117", "(Ununseptium)", "Uus", "17", "7", "p", "", "Synthetic", ""], ["59", "Praseodymium", "Pr", "3", "6", "f", "Solid", "Primordial", "Lanthanide"], ["68", "Erbium", "Er", "3", "6", "f", "Solid", "Primordial", "Lanthanide"], ["67", "Holmium", "Ho", "3", "6", "f", "Solid", "Primordial", "Lanthanide"], ["98", "Californium", "Cf", "3", "7", "f", "Solid", "Transient", "Actinide"], ["92", "Uranium", "U", "3", "7", "f", "Solid", "Primordial", "Actinide"], ["118", "(Ununoctium)", "Uuo", "18", "7", "p", "", "Synthetic", ""], ["61", "Promethium", "Pm", "3", "6", "f", "Solid", "Transient", "Lanthanide"], ["63", "Europium", "Eu", "3", "6", "f", "Solid", "Primordial", "Lanthanide"], ["101", "Mendelevium", "Md", "3", "7", "f", "Solid", "Synthetic", "Actinide"], ["89", "Actinium", "Ac", "3", "7", "f", "Solid", "Transient", "Actinide"], ["69", "Thulium", "Tm", "3", "6", "f", "Solid", "Primordial", "Lanthanide"], ["87", "Francium", "Fr", "1", "7", "s", "Solid", "Transient", "Alkali metal"], ["12", "Magnesium", "Mg", "2", "3", "s", "Solid", "Primordial", "Alkaline earth metal"], ["4", "Beryllium", "Be", "2", "2", "s", "Solid", "Primordial", "Alkaline earth metal"], ["17", "Chlorine", "Cl", "17", "3", "p", "Gas", "Primordial", "Halogen"], ["11", "Sodium", "Na", "1", "3", "s", "Solid", "Primordial", "Alkali metal"], ["71", "Lutetium", "Lu", "3", "6", "d", "Solid", "Primordial", "Lanthanide"]]	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 element comes after iodine?	Xenon	128	Answer:
Table InputTable: [["Year", "Name", "Year", "Name", "Year", "Name"], ["1929-30", "Mr K.Turner", "1961-62", "Mr J.L.Viljoen", "", ""], ["1908-09", "Mr T.R.Ziervogel", "1940-41", "Mr P.Venter", "1972-73", "Mr Ben Steyn"], ["1925-26", "Mr S.Steenberg", "1957- 58", "", "1990-91", "Mr Gerrie Wolmarans"], ["1921-21", "Mr B.Melman", "1953-54", "Mr Vic Pretorius", "1986-87", ""], ["1920-21", "Mr B.Melman", "1952-53", "Mr Vic Pretorius", "1985-86", "Mr J.Prins"], ["1931-32", "Mr A.Zaretsky", "1963-64", "Mr F.J.Van Heerden", "", ""], ["1905-06", "Mr G. Constable", "1937 -38", "", "1969-70", ""], ["1919-20", "Mr B.Melman", "1951-52", "Mr P.Venter", "1984-85", ""], ["1934-34", "", "1966-67", "Mr H.McLennan", "", ""], ["1927-28", "Mr J.Stanbury", "1959-60", "Mr A.P.Scribante", "1992-93", "Mr Gerrie Wolmarans"], ["1923-24", "", "1954-56", "Mr J.H.A.Roets", "1988-89", "Mr Beyers De Klerk"], ["1930-31", "Mr J.E.Bigwood", "1962-63", "Mrs S.Von Wielligh", "", ""], ["1916-17", "Mr R.Champion", "1948-49", "Mrs S.Von Wielligh", "1981-82", "Mr Wiek Steyn"], ["1918-19", "Mr J.Campbell", "1950-51", "Mr P.Venter", "1983-84", ""], ["1933-34", "", "1965-66", "", "", ""], ["1917-18", "Mr A.Ruffels", "1949-50", "Mr A.J.Law", "1982-83", "Mr Andrew Wheeler"], ["1910-11", "", "1942-43", "Mr P.Venter", "1974-75", ""], ["1924-25", "Mr E.Murton", "1956-57", "Mr P.H.Tredoux", "1989-90", "Mr Gerrie Wolmarans"], ["1926-27", "", "1958-59", "Mr J.M.Cawood", "1991-92", "Mr TJ Ferreira"], ["1903-04", "Mr B. Owen- Jones", "1935-36", "Mr W.Pearce", "1967-68", "Mr J.F.Serfontein"], ["1932-33", "Mr G.J.Malan", "1964-65", "", "", ""], ["1915-16", "Mr R.Champion", "1947-48", "Mr C.Chambers", "1980-81", "Mr Meyer"], ["1922-23", "Mr J.Campbell", "1954-55", "", "1987-88", ""], ["1928-29", "Mr E.Murton", "1960-61", "Mr J.L.Viljoen", "1993-94", "Mr TJ Ferreira"], ["1904-05", "Mr B. Owen- Jones", "1936-37", "Mr W.Pearce", "1968-69", "Mr Ben Steyn"], ["1912-13", "Mr J.Johnston", "1944-45", "Mrs E.Myer", "1977-78", "Mr Sakkie Blanche"], ["1907-08", "Mr T.R.Ziervogel", "1939 -40", "Mr W.E.Vickers", "1971-72", "Mr Chris Smith"], ["1911-12", "Mr B.Owen- Jones", "1943-44", "Mr P.Venter", "1975-76", "Mr Sakkie Blanche"], ["1914-15", "Mr J.Cook", "1946-47", "Mrs E.Myer", "1979 -80", "Mr Kobus Durand"], ["1913-14", "Mr J.Cook", "1945-46", "Mrs E.Myer", "1978-79", ""]]	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 mayor to serve in the 1920s?	Mr K.Turner	128	Answer:
Table InputTable: [["Year", "Organization", "Award", "Work", "Result"], ["2007", "10th Nikkan Sports Drama Grand Prix", "Best Actress", "Hana Yori Dango 2", "Won"], ["2007", "16th Hashida Awards", "Newcomer Award", "Hana Yori Dango 2", "Won"], ["2010", "Nikkan Sports Grand Prix (Fall)", "Best Supporting Actress", "Veterinarian Dolittle", "Nominated"], ["2011", "26th Nikkan Sport Film Awards", "Best Newcomer", "Youkame no Semi, Miracle in the Pacific", "Won"], ["2011", "TV Navi", "Best Actress", "Ohisama", "Won"], ["2012", "16th Nikkan Sport Grand Prix", "Best Actress", "Tokkan", "Nominated"], ["2008", "Nickelodeon Kids' Choice Awards", "Best Actress", "Hana Yori Dango 2", "Won"], ["2007", "2007 MTV Student Voice Awards", "Best Actress", "Hana Yori Dango 2", "Won"], ["2011", "3rd TAMA Film Award", "Best Emerging Actress", "Miracle in the Pacific", "Won"], ["2011", "70th The Television Drama Academy Awards", "Best Actress", "Ohisama", "Won"], ["2012", "Japan Film Festival Theater Staff", "Best Actress", "Youkame no Semi", "Won"], ["2012", "35th Japan Academy Awards", "Best Starring Actress", "Youkame no Semi", "Won"], ["2011", "35th Fumiko Yamaji Award Film Awards", "Newcomer Actress", "Youkame no Semi", "Won"], ["2005", "47th The Television Drama Academy Awards", "Best Actress", "Hana Yori Dango", "Won"], ["2007", "54th The Television Academy Drama Awards", "Best Actress", "First Kiss", "Nominated"]]	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:where there at least 10 wins?	yes	128	Answer:
Table InputTable: [["Client", "Years of Operation", "Area of Operation", "Country", "Services"], ["Marathon", "1998", "Manzala field", "Egypt", "Drilling"], ["Lasmo", "1993-94", "Wadi Borjuj", "Libya", "Drilling, workover"], ["Agoco", "1991-current", "Sarir field", "Libya", "Drilling, workover"], ["Waha", "1994", "El Zahra", "Libya", "Drilling, workover"], ["OMV", "1997", "Field 103", "Libya", "Drilling, workover"], ["Khalda/Repsol", "1998-99", "West Desert", "Egypt", "Drilling"], ["SOC", "2000-current", "SOC fields", "Libya", "Drilling"], ["IPC-Dublin", "1997-97", "Kilwa", "Tanzania", "Drilling"], ["Agiba-Agip", "1999", "West Desert", "Egypt", "Drilling"], ["Veba", "2000", "Different fields", "Libya", "Drilling, workover"], ["Fina", "1997", "El Hamada", "Libya", "Drilling, workover"], ["Total", "1999-current", "El Mabrouk", "Libya", "Drilling"], ["Zueitina", "2001-current", "Field 103", "Libya", "Drilling, workover"], ["Perenco Oil Co.", "2000-01", "EchiraX Concession", "Gabon", "Drilling"], ["IPLL", "1999-current", "El Naka field", "Libya", "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:how many years did marathon stay in operation?	1	128	Answer:
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["1999", "European Junior Championships", "Riga, Latvia", "4th", "400 m hurdles", "52.17"], ["2003", "European U23 Championships", "Bydgoszcz, Poland", "1st", "400 m hurdles", "48.45"], ["2003", "European U23 Championships", "Bydgoszcz, Poland", "1st", "4x400 m relay", "3:03.32"], ["2000", "World Junior Championships", "Santiago, Chile", "1st", "400 m hurdles", "49.23"], ["2012", "European Championships", "Helsinki, Finland", "18th (sf)", "400 m hurdles", "50.77"], ["2006", "European Championships", "Gothenburg, Sweden", "2nd", "400 m hurdles", "48.71"], ["2002", "European Indoor Championships", "Vienna, Austria", "1st", "400 m", "45.39 (CR, NR)"], ["2002", "European Championships", "Munich, Germany", "4th", "400 m", "45.40"], ["2002", "European Championships", "Munich, Germany", "8th", "4x400 m relay", "DQ"], ["2004", "Olympic Games", "Athens, Greece", "6th", "400 m hurdles", "49.00"], ["2002", "European Indoor Championships", "Vienna, Austria", "1st", "4x400 m relay", "3:05.50 (CR)"], ["2001", "Universiade", "Beijing, China", "8th", "400 m hurdles", "49.68"], ["2007", "World Championships", "Osaka, Japan", "3rd", "400 m hurdles", "48.12 (NR)"], ["2003", "World Indoor Championships", "Birmingham, United Kingdom", "7th (sf)", "400 m", "46.82"], ["2004", "Olympic Games", "Athens, Greece", "10th (h)", "4x400 m relay", "3:03.69"], ["2003", "World Indoor Championships", "Birmingham, United Kingdom", "3rd", "4x400 m relay", "3:06.61"], ["2001", "World Championships", "Edmonton, Canada", "18th (sf)", "400 m hurdles", "49.80"], ["2007", "World Championships", "Osaka, Japan", "3rd", "4x400 m relay", "3:00.05"], ["2008", "Olympic Games", "Beijing, China", "6th", "400 m hurdles", "48.42"], ["2008", "Olympic Games", "Beijing, China", "7th", "4x400 m relay", "3:00.32"]]	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 which competition did plawgo compete after the 1999 european junior championships?	World Junior Championships	128	Answer:
Table InputTable: [["Week", "Date", "Opponent", "Result", "Attendance"], ["1", "September 13, 1987", "San Diego Chargers", "W 20–13", "56,940"], ["6", "October 25, 1987", "at San Diego Chargers", "L 42–21", "47,972"], ["14", "December 19, 1987", "at Denver Broncos", "L 20–17", "75,053"], ["5", "October 18, 1987", "Denver Broncos", "L 26–17", "20,296"], ["10", "November 22, 1987", "Green Bay Packers", "L 23–3", "34,611"], ["15", "December 27, 1987", "Seattle Seahawks", "W 41–20", "20,370"], ["7", "November 1, 1987", "at Chicago Bears", "L 31–28", "63,498"], ["12", "December 6, 1987", "at Cincinnati Bengals", "L 30–27", "46,489"], ["4", "October 11, 1987", "at Miami Dolphins", "L 42–0", "25,867"], ["8", "November 8, 1987", "Pittsburgh Steelers", "L 17–16", "45,249"], ["13", "December 13, 1987", "Los Angeles Raiders", "W 16–10", "63,834"], ["11", "November 26, 1987", "at Detroit Lions", "W 27–20", "43,820"], ["2", "September 20, 1987", "at Seattle Seahawks", "L 43–14", "61,667"], ["3", "October 4, 1987", "at Los Angeles Raiders", "L 35–17", "10,708"], ["–", "September 27, 1987", "Minnesota Vikings", "canceled", ""], ["9", "November 15, 1987", "New York Jets", "L 16–9", "40,718"]]	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 times did they face the san diego chargers?	2	128	Answer:
Table InputTable: [["Rank", "Heat", "Name", "Nationality", "Time", "Notes"], ["2", "3", "Reanchai Srihawong", "Thailand", "10.72", "Q"], ["2", "4", "Saad Faraj Al-Shahwani", "Qatar", "10.67", "Q"], ["", "3", "Sudath Ravindra Kumara", "Sri Lanka", "DQ", ""], ["2", "2", "Yuta Kanno", "Japan", "10.64", "Q"], ["7", "2", "Tang Yui Han", "Singapore", "11.61", "PB"], ["2", "1", "Hiroyasu Tsuchie", "Japan", "10.64", "Q"], ["6", "2", "Ahmad Hudeib Al-Mamari", "Oman", "10.97", ""], ["1", "2", "Gennadiy Chernovol", "Kazakhstan", "10.59", "Q"], ["6", "1", "Vissanu Sophanich", "Thailand", "10.87", ""], ["8", "2", "Chaleunsouk Oudomphanh", "Laos", "11.76", "SB"], ["4", "1", "Chintake De Zoysa", "Sri Lanka", "10.78", "q"], ["5", "1", "Suminda Mendis", "Sri Lanka", "10.82", "q, PB"], ["", "4", "Hamood Al-Dalhami", "Oman", "DQ", ""], ["4", "2", "Tsai Meng-Lin", "Chinese Taipei", "10.74", "q"], ["3", "4", "Azmi Ibrahim", "Malaysia", "10.78", "Q"], ["3", "2", "Shen Yunbao", "China", "10.72", "Q"], ["7", "4", "Bona Kong", "Cambodia", "11.75", "PB"], ["6", "4", "Piphop Rasme Prum Keo", "Cambodia", "11.70", "PB"], ["5", "4", "Nguyen Thanh Hai", "Vietnam", "11.16", "PB"], ["5", "2", "Tan Kok Lim", "Malaysia", "10.83", "q"], ["1", "3", "Jamal Al-Saffar", "Saudi Arabia", "10.57", "Q"], ["4", "4", "Chiang Wai Hung", "Malaysia", "10.89", ""], ["3", "3", "Shin Jung-Ki", "South Korea", "10.79", ""], ["", "1", "Khalil Al-Hanahneh", "Jordan", "DNS", ""], ["5", "3", "To Wai Lok", "Hong Kong", "10.92", ""], ["7", "1", "Zakaria Messaiké", "Lebanon", "11.06", ""], ["7", "3", "Abdullah Ibrahim", "Maldives", "12.04", "PB"], ["1", "1", "Salem Al-Yami", "Saudi Arabia", "10.55", "Q"], ["3", "1", "Khaled Yousef Al-Obaidli", "Qatar", "10.68", "Q"], ["4", "3", "Chen Tien-Wen", "Chinese Taipei", "10.74", "q"], ["6", "3", "Poh Seng Song", "Singapore", "11.10", "SB"], ["1", "4", "Chen Haijian", "China", "10.65", "Q"]]	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 at the top of the second best?	Hiroyasu Tsuchie	128	Answer:
Table InputTable: [["Release date", "Album Title", "Record Company", "Song Title", "Certification"], ["March 5, 2011", "Kris Aquino: My Heart’s Journey", "Universal Records", "\"God Bless the Broken Road\"", "PARI: Platinum"], ["April 2009", "OPM Number 1's", "Star Records", "\"Can't Hurry Love\"", "PARI:"], ["June 24, 2009", "I Move, I Give, I Love", "Star Records", "\"Power of the Dream\", \"Bagong Umaga\" with Erik Santos & Yeng Constantino", "PARI: Gold"], ["February 2011", "I Love You", "Star Records", "\"Catch Me I'm Falling\"", "PARI:"], ["November 12, 2011", "Happy Yipee Yehey! Nananana!", "Star Records", "\"Mahalin Ka Ng Totoo\"", "PARI: Gold"], ["November 18, 2011", "Da Best ang Pasko ng Pilipino", "Star Records", "\"Ganyan ang Pasko\", \"Ngayong Pasko Magniningning ang Pilipino\" (Solo) & with Gary Valenciano", "PARI:"], ["July 25, 2007", "Nagmamahal, Kapamilya: Songs for Global Pinoys", "Star Records", "\"Super Pinoy\"", "PARI: 6X Platinum"], ["December 2007", "H.O.P.E. (Healing Of Pain and Enlightenment)", "Star Records", "\"Count On Me\"", "PARI: Gold"], ["June 2011", "Bida Best Hits Da Best", "Star Records", "\"Mahal Kita Kasi\", \"Catch Me I'm Falling\", \"You Are The One\" with Sam Milby", "PARI:"], ["January 2011", "OPM Number 1's Vol. 2", "Star Records", "\"All Me (Remix)\"", "PARI:"], ["November 2010", "Ngayong Pasko Magniningning ang Pilipino: Christmas Songs Compilation", "Star Records", "\"Ganyan ang Pasko\", \"Ngayong Pasko Magniningning ang Pilipino\" (Solo) & with Gary Valenciano", "PARI:"], ["October 8, 2006", "Hotsilog: The ASAP Hotdog Compilation", "ASAP Music", "\"Annie Batungbakal\"", "PARI: Platinum"], ["January 17, 2013", "Himig Handog P-Pop Love Songs 2013", "Star Records", "\"Kahit Na\"", "PARI:"], ["June 2010", "60 Taon ng Musika at Soap Opera", "Star Records", "\"Crazy For You\"", "PARI:"]]	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 album toni gonzaga participated in that was below platinum status?	H.O.P.E. (Healing Of Pain and Enlightenment)	128	Answer:
Table InputTable: [["decimal32", "decimal64", "decimal128", "decimal(32k)", "Format"], ["32", "64", "128", "32×k", "Total size (bits)"], ["7", "16", "34", "p = 3×t/10+1 = 9×k−2", "Coefficient size (decimal digits)"], ["96", "384", "6144", "Emax = 3×2w−1", "Largest value is 9.99...×10Emax"], ["192", "768", "12288", "3×2w = 48×4k", "Exponent range"], ["−101", "−398", "−6176", "Etiny = 2−p−Emax", "Smallest non-zero value is 1×10Etiny"], ["−95", "−383", "−6143", "Emin = 1−Emax", "Smallest normalized value is 1.00...×10Emin"], ["1", "1", "1", "1", "Sign field (bits)"], ["6", "8", "12", "w = 2×k + 4", "Exponent continuation field (bits)"], ["5", "5", "5", "5", "Combination field (bits)"], ["20", "50", "110", "t = 30×k−10", "Coefficient continuation field (bits)"]]	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:name the only format with a decimal 128 value above 10,000.	Exponent range	128	Answer:
Table InputTable: [["Tie no", "Home team", "Score", "Away team", "Date", "Attendance"], ["Replay", "Millwall", "1 – 2", "Southampton", "5 February 2003", "10,197"], ["Replay", "Liverpool", "0 – 2", "Crystal Palace", "5 February 2003", "35,109"], ["Replay", "Sunderland", "2 – 2", "Blackburn Rovers", "5 February 2003", "15,745"], ["Replay", "Leeds United", "2 – 1", "Gillingham", "4 February 2003", "29,359"], ["2", "Southampton", "1 – 1", "Millwall", "25 January 2003", "23,809"], ["4", "Walsall", "2 – 0", "Wimbledon", "25 January 2003", "6,693"], ["15", "Farnborough Town", "1 – 5", "Arsenal", "25 January 2003", "35,108"], ["14", "Crystal Palace", "0 – 0", "Liverpool", "26 January 2003", "26,054"], ["1", "Rochdale", "2 – 0", "Coventry City", "25 January 2003", ""], ["7", "Wolverhampton Wanderers", "4 – 1", "Leicester City", "25 January 2003", "28,164"], ["11", "Brentford", "0 – 3", "Burnley", "25 January 2003", "9,563"], ["5", "Gillingham", "1 – 1", "Leeds United", "25 January 2003", "11,093"], ["8", "Shrewsbury Town", "0 – 4", "Chelsea", "26 January 2003", "7,950"], ["9", "Sheffield United", "4 – 3", "Ipswich Town", "25 January 2003", "12,757"], ["6", "Blackburn Rovers", "3 – 3", "Sunderland", "25 January 2003", "14,315"], ["10", "Fulham", "3 – 0", "Charlton Athletic", "26 January 2003", "12,203"], ["16", "Stoke City", "3 – 0", "Bournemouth", "26 January 2003", "12,004"], ["3", "Watford", "1 – 0", "West Bromwich Albion", "25 January 2003", "16,975"], ["13", "Norwich City", "1 – 0", "Dagenham & Redbridge", "25 January 2003", "21,164"], ["12", "Manchester United", "6 – 0", "West Ham United", "26 January 2003", "67,181"]]	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 replay games took place?	4	128	Answer:
Table InputTable: [["#", "Stadium", "Capacity", "City", "Region", "Home Team", "Opened"], ["4", "Parc des Princes", "48,712", "Paris", "Île-de-France", "Paris Saint-Germain FC", "1897"], ["1", "Stade de France", "81,338", "Paris", "Île-de-France", "France national football team", "1998"], ["27", "Stade Sébastien Charléty", "20,000", "Paris", "Île-de-France", "Paris FC", "1938"], ["12", "Stade de la Mosson", "32,939", "Montpellier", "Languedoc-Roussillon", "Montpellier HSC", "1972"], ["14", "Stade de la Meinau", "29,230", "Strasbourg", "Alsace", "RC Strasbourg", "1914"], ["10", "Stadium Municipal", "35,575", "Toulouse", "Midi-Pyrénées", "Toulouse FC", "1937"], ["7", "Stade de la Beaujoire", "38,285", "Nantes", "Pays de la Loire", "FC Nantes Atlantique", "1984"], ["26", "Stade Auguste Bonal", "20,025", "Montbéliard", "Franche-Comté", "FC Sochaux-Montbéliard", "2000"], ["16", "Grand Stade du Havre", "25,178", "Le Havre", "Upper Normandy", "Le Havre AC", "2012"], ["11", "Stade Chaban-Delmas", "34,462", "Bordeaux", "Aquitaine", "FC Girondins de Bordeaux", "1938"], ["21", "Stade Auguste-Delaune", "21,684", "Reims", "Champagne-Ardenne", "Stade Reims", "1935"], ["13", "Stade de la Route de Lorient", "31,127", "Rennes", "Brittany", "Stade Rennais FC", "1912"], ["22", "Stade Michel d'Ornano", "21,500", "Caen", "Lower Normandy", "Stade Malherbe Caen", "1993"], ["23", "Stade de l'Aube", "20,400", "Troyes", "Champagne-Ardenne", "Troyes AC", "1956"], ["20", "Stade Louis Dugauguez", "23,189", "Sedan", "Champagne-Ardenne", "Club Sportif Sedan Ardennes", "2000"], ["25", "Stade des Alpes", "20,068", "Grenoble", "Rhône-Alpes", "Grenoble Foot 38", "2008"], ["19", "Stade de l'Abbé-Deschamps", "23,467", "Auxerre", "Bourgogne", "AJ Auxerre", "1918"], ["18", "Stade du Hainaut", "24,926", "Valenciennes", "Nord-Pas-de-Calais", "Valenciennes FC", "2011"], ["6", "Stade Gerland", "41,044", "Lyon", "Rhône-Alpes", "Olympique Lyonnais", "1926"], ["15", "Stade Municipal Saint-Symphorien", "26,700", "Metz", "Lorraine", "FC Metz", "1923"], ["3", "Grand Stade Lille Métropole", "50,186", "Villeneuve-d'Ascq", "Nord-Pas-de-Calais", "Lille OSC", "2012"], ["2", "Stade Vélodrome", "60,013", "Marseille", "Provence-Alpes-Côte d'Azur", "Olympique de Marseille", "1937"], ["5", "Stade Félix Bollaert", "41,233", "Lens", "Nord-Pas-de-Calais", "RC Lens", "1932"], ["8", "Stade Geoffroy-Guichard", "37,587", "Saint-Étienne", "Rhône-Alpes", "AS Saint-Étienne", "1931"], ["17", "MMArena", "25,000", "Le Mans", "Pays de la Loire", "Le Mans UC", "2011"], ["24", "Stade Marcel Picot", "20,087", "Tomblaine", "Lorraine", "AS Nancy", "1926"]]	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 stadium was the last to be opened?	Allianz Riviera	128	Answer:
Table InputTable: [["Year", "Competition", "Venue", "Position", "Notes"], ["2011", "World Championships", "Daegu, South Korea", "9th", "5.65 m"], ["2009", "World Championships", "Berlin, Germany", "22nd (q)", "5.40 m"], ["2010", "European Championships", "Barcelona, Spain", "10th", "5.60 m"], ["2012", "European Championships", "Helsinki, Finland", "6th", "5.60 m"], ["2008", "Olympic Games", "Beijing, China", "10th", "5.45 m"], ["2009", "European U23 Championships", "Kaunas, Lithuania", "8th", "5.15 m"], ["2005", "World Youth Championships", "Marrakech, Morocco", "6th", "5.05 m"], ["2006", "World Junior Championships", "Beijing, China", "5th", "5.30 m"], ["2014", "World Indoor Championships", "Sopot, Poland", "3rd", "5.80 m"], ["2013", "European Indoor Championships", "Gothenburg, Sweden", "5th", "5.71 m"], ["2012", "Olympic Games", "London, United Kingdom", "8th", "5.65 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 competition had the least highest amount listed under notes?	World Youth Championships	128	Answer:
Table InputTable: [["Year", "Competition", "Venue", "Position", "Notes"], ["2012", "European Championships", "Helsinki, Finland", "6th", "5.60 m"], ["2011", "World Championships", "Daegu, South Korea", "9th", "5.65 m"], ["2009", "World Championships", "Berlin, Germany", "22nd (q)", "5.40 m"], ["2009", "European U23 Championships", "Kaunas, Lithuania", "8th", "5.15 m"], ["2006", "World Junior Championships", "Beijing, China", "5th", "5.30 m"], ["2005", "World Youth Championships", "Marrakech, Morocco", "6th", "5.05 m"], ["2012", "Olympic Games", "London, United Kingdom", "8th", "5.65 m"], ["2010", "European Championships", "Barcelona, Spain", "10th", "5.60 m"], ["2008", "Olympic Games", "Beijing, China", "10th", "5.45 m"], ["2014", "World Indoor Championships", "Sopot, Poland", "3rd", "5.80 m"], ["2013", "European Indoor Championships", "Gothenburg, Sweden", "5th", "5.71 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:how many times total did jan kudlička place 6th while competing in the olympic games?	0	128	Answer:
Table InputTable: [["Rank", "Mountain Peak", "Nation", "Province", "Elevation", "Prominence", "Isolation"], ["8", "Montañas Peña Blanca High Point PB", "Guatemala", "Huehuetenango", "3518 m\\n11,542 ft", "1858 m\\n6,096 ft", "42 km\\n26 mi"], ["4", "Cerro las Minas PB", "Honduras", "Lempira", "2849 m\\n9,347 ft", "2069 m\\n6,788 ft", "130 km\\n81 mi"], ["1", "Volcán Tajumulco PB", "Guatemala", "San Marcos", "4220 m\\n13,845 ft", "3980 m\\n13,058 ft", "722 km\\n448 mi"], ["14", "Montaña San Ildefonso PB", "Honduras", "Cortés", "2242 m\\n7,356 ft", "1702 m\\n5,584 ft", "68 km\\n42 mi"], ["15", "Volcán San Cristóbal PB", "Nicaragua", "Chinandega", "1745 m\\n5,725 ft", "1665 m\\n5,463 ft", "134 km\\n83 mi"], ["11", "Cerro Tacarcuna PB", "Panama", "Darién", "1875 m\\n6,152 ft", "1770 m\\n5,807 ft", "99 km\\n61 mi"], ["9", "Volcán Acatenango PB", "Guatemala", "Chimaltenango\\nSacatepéquez", "3975 m\\n13,041 ft", "1835 m\\n6,020 ft", "126 km\\n78 mi"], ["12", "Volcán Atitlán PB", "Guatemala", "Sololá", "3537 m\\n11,604 ft", "1754 m\\n5,755 ft", "35 km\\n21 mi"], ["3", "Montaña de Santa Bárbara PB", "Honduras", "Santa Bárbara", "2744 m\\n9,003 ft", "2084 m\\n6,837 ft", "74 km\\n46 mi"], ["5", "Volcán de Agua PB", "Guatemala", "Escuintla\\nSacatepéquez", "3761 m\\n12,339 ft", "1981 m\\n6,499 ft", "16 km\\n10 mi"], ["7", "Volcán Irazú PB", "Costa Rica", "Cartago\\nSan José", "3402 m\\n11,161 ft", "1872 m\\n6,142 ft", "48 km\\n30 mi"], ["6", "Alto Cuchumatanes PB", "Guatemala", "Huehuetenango", "3837 m\\n12,589 ft", "1877 m\\n6,158 ft", "65 km\\n40 mi"], ["2", "Chirripó Grande PB", "Costa Rica", "Cartago\\nLimón\\nSan José", "3819 m\\n12,530 ft", "3726 m\\n12,224 ft", "864 km\\n537 mi"], ["13", "Pico Bonito PB", "Honduras", "Atlántida", "2450 m\\n8,038 ft", "1710 m\\n5,610 ft", "152 km\\n95 mi"], ["10", "Volcán San Miguel PB", "El Salvador", "San Miguel", "2131 m\\n6,991 ft", "1831 m\\n6,007 ft", "64 km\\n40 mi"]]	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 elevation of the rank 1 mountain?	4220 m, 13,845 ft	128	Answer:
Table InputTable: [["Station", "Station Connections", "Date Opened", "Station Parking", "City/ Neighborhood"], ["Norwalk", "Metro Local: 111, 115, 120, 125, 270, 311\\nMetro Express: 460, 577X\\nNorwalk Transit: 2, 4, 5\\nLong Beach Transit: 172, 173", "August 12, 1995", "2050 Spaces", "Norwalk"], ["Hawthorne/Lennox", "Metro Local: 40, 126, 212, 312\\nMetro Express: 442\\nMetro Rapid: 740", "August 12, 1995", "623 Spaces", "Hawthorne"], ["Avalon", "Metro Local: 48, 51, 52, 53, 352\\nLADOT DASH: Watts\\nWillowbrook Shuttle", "August 12, 1995", "158 Spaces", "South Los Angeles"], ["Willowbrook", "Metro Blue Line \\nMetro Local: 55, 120, 205, 355, 612\\nGardena Transit: 5\\nLADOT DASH: Watts\\nLynwood Breeze Route D\\nWillowbrook Shuttle: A, B, King Medical Center", "August 12, 1995", "975 Spaces", "Willowbrook"], ["Crenshaw", "Metro Local: 126, 207 (Weekdays selected Rush Hour AM/PM trips, & Weekends Only), 210\\nMetro Rapid: 710, 757\\nTorrance Transit: 5, 10", "August 12, 1995", "513 Spaces", "Hawthorne"], ["Long Beach Boulevard", "Metro Local: 60, 251\\nMetro Rapid: 760\\nLynwood Trolley: A", "August 12, 1995", "650 Spaces", "Lynwood"], ["Redondo Beach", "Metro Local: 126, 215\\nBeach Cities Transit 102\\nLADOT Commuter Express: 574\\nLawndale Beat Express, Residential Routes", "August 12, 1995", "403 Spaces", "Redondo Beach"], ["Vermont/Athens", "Metro Local: 204, 206, 209\\nMetro Rapid: 754\\nGardena Transit:2", "August 12, 1995", "155 Spaces", "Athens"], ["Harbor Freeway", "Metro Silver Line \\nMetro Local: 45, 81, 120\\nMetro Express: 450 (Weekdays Rush Hour AM/PM trips, & Sundays Only), 550\\nMetro Rapid: 745\\nLADOT Commuter Express: 448\\nOrange County Transportation Authority: 721\\nGardena Transit: 1X\\nTorrance Transit: 1, 2, 4", "August 12, 1995", "253 Spaces", "South Los Angeles"], ["Douglas", "Metro Local: 125\\nAmtrak California Thruway Motorcoach: Route 1C\\nBeach Cities Transit: 109 (northbound only)", "August 12, 1995", "30 Spaces", "El Segundo"], ["Aviation/LAX", "Metro Local: 40 Owl, 120, 625\\nLAX Shuttle: Route G (serves LAX Terminals 1-8 and the Tom Bradley International Terminal)\\nSanta Monica Transit: 3, Rapid 3\\nCulver City Transit: 6, Rapid 6\\nBeach Cities Transit: 109\\nMunicipal Area Express: 2, 3, 3X", "August 12, 1995", "405 Spaces", "El Segundo"], ["Lakewood Boulevard", "Metro Local: 117, 265, 266", "August 12, 1995", "545 Spaces", "Downey"], ["El Segundo", "Gardena Transit: 5\\nLADOT Commuter Express: 574\\nMunicipal Area Express: 2, 3, 3X\\nTorrance Transit: 8 (Southbound)", "August 12, 1995", "90 Spaces", "El Segundo"], ["Mariposa", "Metro Local: 232\\nTorrance Transit: 8 (Southbound)", "August 12, 1995", "No parking", "El Segundo"]]	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 station has the most parking spaces?	Norwalk	128	Answer:
Table InputTable: [["No.", "Score", "Player", "Team", "Balls", "Inns.", "Opposing team", "Date", "Result"], ["31", "120", "Sunil Gavaskar (1/3)", "India", "NR", "1", "West Indies", "24 January 1979", "Drawn"], ["52", "122", "Sachin Tendulkar (1/2)", "India", "233", "2", "Zimbabwe", "18 November 2000", "Won"], ["34", "115", "Sunil Gavaskar (2/3)", "India", "238", "1", "Australia", "13 October 1979", "Drawn"], ["32", "109", "Dilip Vengsarkar (1/4)", "India", "NR", "1", "West Indies", "24 January 1979", "Drawn"], ["54", "109", "Sachin Tendulkar (2/2)", "India", "196", "1", "Sri Lanka", "10 December 2005", "Won"], ["36", "100", "Yashpal Sharma", "India", "239", "1", "Australia", "13 October 1979", "Drawn"], ["46", "109", "Viv Richards (2/2)", "West Indies", "111", "4", "India", "25 November 1987", "Won"], ["33", "126", "Kapil Dev", "India", "124", "1", "West Indies", "24 January 1979", "Drawn"], ["51", "200", "Rahul Dravid", "India", "350", "2", "Zimbabwe", "18 November 2000", "Won"], ["35", "131", "Gundappa Viswanath (1/2)", "India", "207", "1", "Australia", "13 October 1979", "Drawn"], ["29", "192", "Viv Richards (1/2)", "West Indies", "NR", "2", "India", "11 December 1974", "Won"], ["44", "160", "Tim Robinson", "England", "390", "2", "India", "12 December 1984", "Won"], ["37", "146", "Dilip Vengsarkar (2/4)", "India", "370", "4", "Pakistan", "4 December 1979", "Drawn"], ["3", "128", "Everton Weekes", "West Indies", "NR", "1", "India", "10 November 1948", "Drawn"], ["26", "106", "Dilip Sardesai", "India", "NR", "2", "New Zealand", "19 March 1965", "Won"], ["49", "152", "Nayan Mongia", "India", "366", "2", "Australia", "10 October 1996", "Won"], ["7", "164", "Vijay Hazare", "India", "NR", "2", "England", "2 November 1951", "Drawn"], ["41", "121", "Sunil Gavaskar (3/3)", "India", "128", "1", "West Indies", "29 October 1983", "Drawn"], ["56", "200", "VVS Laxman", "India", "301", "1", "Australia", "29 October 2008", "Drawn"], ["20", "189", "Vijay Manjrekar (2/2)", "India", "NR", "1", "England", "13 December 1961", "Drawn"], ["55", "206", "Gautam Gambhir", "India", "380", "1", "Australia", "29 October 2008", "Drawn"], ["22", "105", "Hanumant Singh", "India", "NR", "1", "England", "8 February 1964", "Drawn"], ["5", "114", "Hemu Adhikari", "India", "NR", "2", "West Indies", "10 November 1948", "Drawn"], ["24", "100", "Budhi Kunderan", "India", "NR", "3", "England", "8 February 1964", "Drawn"], ["16", "114", "Neil Harvey", "Australia", "NR", "2", "India", "12 December 1959", "Won"], ["6", "154", "Vijay Merchant", "India", "NR", "2", "England", "2 November 1951", "Drawn"], ["42", "159", "Dilip Vengsarkar (3/4)", "India", "238", "1", "West Indies", "29 October 1983", "Drawn"]]	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 recorded balls during the india vs. australia match in 1979	684	128	Answer:
Table InputTable: [["Series #", "Season #", "Title", "Notes", "Original air date"], ["6", "1", "\"Where's the Snake?\"", "Dee Dee gets a snake, but he doesn't want his parents to know about it. However, things get complicated when he loses the snake in the house. Meanwhile, Melanie and Deonne are assigned by their teacher to take care of her beloved pet rabbit, Duchess for the weekend. This causes both Alfie and Dee Dee to be concerned for Duchess when they learn from Goo that snakes eat rabbits.", "December 6, 1994"], ["9", "1", "\"Dee Dee Runs Away\"", "Dee Dee has been waiting to go to a monster truck show all week. But Alfie and Goo's baseball team makes it to the tournament and everyone forgets about the monster truck show. Dee Dee feels ignored and runs away from home with Harry and Donnell. It's up to Alfie and Goo to try and convince him to come home.", "December 28, 1994"], ["11", "1", "\"Alfie's Birthday Party\"", "Goo and Melanie pretend they are dating and they leave Alfie out of everything. He ends up bored and starts hanging out with Dee Dee and his friends. However, it just isn't the same without Goo. Later on, Alfie learns about the surprise birthday party that Goo and Melanie had been planning with everyone else (except for Dee Dee, who couldn't know since he would've told).", "January 19, 1995"], ["5", "1", "\"Basketball Tryouts\"", "Alfie tries out for the basketball team and doesn't make it even after showing off his basketball skills. However, Harry, Dee Dee and Donnell make the team. Alfie is depressed and doesn't want to attend the celebration party. However, Goo sets him straight by telling him it was his own fault for not being a team player and kept the ball to himself.", "November 30, 1994"], ["3", "1", "\"The Weekend Aunt Helen Came\"", "The boy's mother, Jennifer, leaves for the weekend and she leaves the father, Roger, in charge. However, he lets the kids run wild. Alfie and Dee Dee's Aunt Helen then comes to oversee the house until Jennifer gets back. Meanwhile, Alfie throws a basketball at Goo, which hits him in the head, giving him temporary amnesia. In this case of memory loss, Goo acts like a nerd, does homework on a weekend, wants to be called Milton instead of Goo, and he even calls Alfie Alfred. He is much nicer to Deonne and Dee Dee, but is somewhat rude to Melanie. The only thing that will reverse this is another hit in the head.", "November 1, 1994"], ["12", "1", "\"Candy Sale\"", "Alfie and Goo are selling candy to make money for some expensive jackets, but they are not having any luck. However, when Dee Dee start helping them sell candy, they start to make money and asks him to help them out. Soon Goo and Alfie finds themselves confronted by Melanie, Deonne, Harry and Donnell for Dee Dee's share of the money. They soon learn the boys have used the money to buy three expensive jackets for themselves and Dee Dee as a token of their gratitude. They quickly apologize to Alfie and Goo for their quick judgment.", "January 26, 1995"], ["10", "1", "'\"Donnell's Birthday Party\"", "Donnell is having a birthday party and brags about all the dancing and cool people who will be there. Harry says that he knows how to dance so Dee Dee feels left out because he doesn't know how to dance. Later on, Harry admits to Dee Dee alone that he can't dance either and only lied so he doesn't get teased by Donnell. So, they ask Alfie to help them learn how to dance. He refuses to help because Dee Dee previously told on him to Roger about his and Goo's plans to cheat on their math quiz. Alfie eventually agrees, after Melanie threatens to refuse to help him with his math homework. Soon Dee Dee and Harry learn Donnell's secret and were forced to teach him how to dance. After the party, Dee Dee tells Alfie about it and finds out that he knew Donnell was a liar.", "January 5, 1995"], ["13", "1", "\"The Big Bully\"", "Dee Dee gets beat up at school and his friends try to teach him how to fight back. Goo, however, tells him to bluff, but the plan backfires and Dee Dee gets hit because of it. When Alfie confronts the bully, he learns that Dee Dee was picked on by a girl. Alfie and Goo decide to confront her. However, when some of their classmates, who happen to be the girls' siblings, learn they are bullying their sister, they intervene.", "February 2, 1995"]]	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 there in season 1?	13	128	Answer:
Table InputTable: [["State\\n(linked to\\nsummaries below)", "Incumbent\\nSenator", "Incumbent\\nParty", "Incumbent\\nElectoral\\nhistory", "Most recent election results", "2018 intent", "Candidates"], ["New Mexico", "Martin Heinrich", "Democratic", "Martin Heinrich (D) 51.0%\\nHeather Wilson (R) 45.3%\\nJon Barrie (IAP) 3.6%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Nevada", "Dean Heller", "Republican", "Dean Heller (R) 45.9%\\nShelley Berkley (D) 44.7%\\nDavid Lory VanderBeek (C) 4.9%\\nNone of These Candidates 4.5%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Texas", "Ted Cruz", "Republican", "Ted Cruz (R) 56.5%\\nPaul Sadler (D) 40.7%\\nJohn Jay Myers (L) 2.1%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Indiana", "Joe Donnelly", "Democratic", "Joe Donnelly (D) 50.0%\\nRichard Mourdock (R) 44.2%\\nAndrew Horning (L) 5.7%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Virginia", "Tim Kaine", "Democratic", "Tim Kaine (D) 52.9%\\nGeorge Allen (R) 47%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Vermont", "Bernie Sanders", "Independent", "Bernie Sanders (I) 71%\\nJohn MacGovern (R) 24.9%\\nCris Ericson (Marijuana Party) 2%", "2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["North Dakota", "Heidi Heitkamp", "Democratic", "Heidi Heitkamp (D) 50.2%\\nRick Berg (R) 49.3%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["California", "Dianne Feinstein", "Democratic", "Dianne Feinstein (D) 62.5%\\nElizabeth Emken (R) 37.5%", "1992 (special)\\n1994\\n2000\\n2006\\n2012", "Running", "[Data unknown/missing. You can help!]"], ["Arizona", "Jeff Flake", "Republican", "Jeff Flake (R) 49.2%\\nRichard Carmona (D) 46.1%\\nMarc Victor (L) 4.6%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Maryland", "Ben Cardin", "Democratic", "Ben Cardin (D) 56.0%\\nDan Bongino (R) 26.3%\\nS. Rob Sobhani (I) 16.4%", "2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["State\\n(linked to\\nsummaries below)", "Incumbent", "Incumbent", "Incumbent", "Most recent election results", "2018 intent", "Candidates"], ["Massachusetts", "Elizabeth Warren", "Democratic", "Elizabeth Warren (D) 53.7%\\nScott Brown (R) 46.3%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Nebraska", "Deb Fischer", "Republican", "Deb Fischer (R) 57.8%\\nBob Kerrey (D) 42.2%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Wisconsin", "Tammy Baldwin", "Democratic", "Tammy Baldwin (D) 51.4%\\nTommy Thompson (R) 45.5%\\nJoseph Kexel (L) 2.1%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"]]	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 this race summary, which party has the most incumbents?	Democratic	128	Answer:
Table InputTable: [["Distribution", "x86", "x86-64", "ia64", "ppc", "ppc64", "sparc32", "sparc64", "arm", "hppa", "mips", "sh", "s390", "s390x", "alpha", "m68k"], ["Distribution", "x86", "x86-64", "ia64", "ppc", "ppc64", "sparc32", "sparc64", "arm", "hppa", "mips", "sh", "s390", "s390x", "alpha", "m68k"], ["Slackware", "Yes", "Yes", "No", "No", "No", "Discontinued\\n?", "No", "Yes", "No", "No", "No", "Discontinued\\n?", "Discontinued\\n?", "Discontinued\\n8.1", "No"], ["Tor-ramdisk", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "Yes", "No", "No", "No", "No", "No"], ["Red Hat Linux", "Yes", "No", "Discontinued\\n7.1-7.2", "Test release\\n5.1", "No", "Discontinued\\n4.0-4.2\\n5.1-6.2", "Test release\\n5.1", "No", "No", "Test release\\n5.1", "No", "Discontinued\\n7.2", "Discontinued\\n7.1", "Discontinued\\n2.1-7.1", "Test release\\n5.1"], ["MintPPC", "No", "No", "No", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No"], ["Red Hat Enterprise Linux", "Discontinued\\n2.1-6", "Yes\\n3+", "Discontinued\\n2.1-5", "Yes\\n3+", "Yes\\n3+", "No", "No", "No", "No", "No", "No", "Discontinued\\n3-4", "Yes\\n3+", "No", "No"], ["Frugalware", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No"], ["OpenWrt", "Yes", "No", "No", "Yes", "No", "No", "No", "Yes", "No", "Yes", "Yes", "No", "No", "No", "No"], ["SUSE Linux Enterprise Server", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "Yes", "No", "No"], ["XBMC", "Yes", "No", "No", "No", "No", "No", "No", "Yes", "No", "No", "No", "No", "No", "No", "No"], ["CentOS", "Yes", "Yes", "Discontinued\\n3.5-3.8\\n4.1-4.7", "Beta\\n4.0", "No", "Beta\\n4.2", "No", "No", "No", "No", "No", "Discontinued\\n3.5-3.8\\n4.1-4.7", "Discontinued\\n3.5-3.8\\n4.1-4.7", "Discontinued\\n4.2-4.3", "No"], ["Scientific Linux", "Yes", "Yes", "Discontinued\\n3-4", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No"], ["Gentoo", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes"], ["openSUSE", "Yes", "Yes", "No", "No", "No", "No", "partial", "partial", "No", "No", "No", "No", "No", "No", "No"], ["Rxart Desktop", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No"]]	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 mant distributors are on the data table	34	128	Answer:
Table InputTable: [["State\\n(linked to\\nsummaries below)", "Incumbent\\nSenator", "Incumbent\\nParty", "Incumbent\\nElectoral\\nhistory", "Most recent election results", "2018 intent", "Candidates"], ["Connecticut", "Chris Murphy", "Democratic", "Chris Murphy (D) 54.8%\\nLinda McMahon (R) 43.1%\\nPaul Passarelli (L) 1.7%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Nevada", "Dean Heller", "Republican", "Dean Heller (R) 45.9%\\nShelley Berkley (D) 44.7%\\nDavid Lory VanderBeek (C) 4.9%\\nNone of These Candidates 4.5%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Montana", "Jon Tester", "Democratic", "Jon Tester (D) 48.6%\\nDenny Rehberg (R) 44.9%\\nDan Cox (L) 6.6%", "2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["California", "Dianne Feinstein", "Democratic", "Dianne Feinstein (D) 62.5%\\nElizabeth Emken (R) 37.5%", "1992 (special)\\n1994\\n2000\\n2006\\n2012", "Running", "[Data unknown/missing. You can help!]"], ["Delaware", "Tom Carper", "Democratic", "Tom Carper (D) 66.4%\\nKevin L. Wade (R) 29.0%\\nAlex Pires (I) 3.8%", "2000\\n2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Pennsylvania", "Bob Casey, Jr.", "Democratic", "Bob Casey, Jr. (D) 53.7%\\nTom Smith (R) 44.6%\\nRayburn Douglas Smith (L) 1.7%", "2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Indiana", "Joe Donnelly", "Democratic", "Joe Donnelly (D) 50.0%\\nRichard Mourdock (R) 44.2%\\nAndrew Horning (L) 5.7%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["West Virginia", "Joe Manchin", "Democratic", "Joe Manchin (D) 60.6%\\nJohn Raese (R) 36.5%\\nBob Henry Baber (G) 3.0%", "2010\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Virginia", "Tim Kaine", "Democratic", "Tim Kaine (D) 52.9%\\nGeorge Allen (R) 47%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Tennessee", "Bob Corker", "Republican", "Bob Corker (R) 64.9%\\nMark E. Clayton (D) 30.4%\\nMartin Pleasant (G) 1.7%", "2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Texas", "Ted Cruz", "Republican", "Ted Cruz (R) 56.5%\\nPaul Sadler (D) 40.7%\\nJohn Jay Myers (L) 2.1%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Rhode Island", "Sheldon Whitehouse", "Democratic", "Sheldon Whitehouse (D) 64.8%\\nBarry Hinckley (R) 35.0%", "2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"]]	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 percentage difference between the r and d candidates in ct?	11.7	128	Answer:
Table InputTable: [["Position", "Sail Number", "Yacht", "State/Country", "Yacht Type", "LOA\\n(Metres)", "Skipper", "Elapsed Time\\nd:hh:mm:ss"], ["4", "AUS70", "Ragamuffin", "NSW", "Farr 50", "15.15", "Syd Fischer", "3:06:11:29"], ["2", "C1", "Brindabella", "NSW", "Jutson 79", "24.07", "George Snow", "2:21:55:06"], ["3", "YC1000", "Ausmaid", "SA", "Farr 47", "14.24", "Kevan Pearce", "3:06:02:29"], ["7", "6606", "Quest", "NSW", "Nelson Marek 46", "14.12", "Bob Steel", "3:14:41:28"], ["8", "9090", "Industrial Quest", "QLD", "Nelson Marek 43", "13.11", "Kevin Miller", "3:14:58:46"], ["9", "4826", "Aspect Computing", "NSW", "Radford 16.5 Sloop", "16.50", "David Pescud", "3:15:28:24"], ["1", "US17", "Sayonara", "USA", "Farr ILC Maxi", "24.13", "Larry Ellison", "2:19:03:32"], ["10", "8338", "AFR Midnight Rambler", "NSW", "Hick 35", "10.66", "Ed Psaltis\\nBob Thomas", "3:16:04:40"], ["6", "SM1", "Fudge", "VIC", "Elliot 56", "17.07", "Peter Hansen", "3:11:00:26"], ["5", "COK1", "Nokia", "CI", "Farr Ketch Maxi", "25.20", "David Witt", "3:09:19:00"]]	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 placed the most yachts in the line honours results?	NSW	128	Answer:
Table InputTable: [["Result", "Date", "Category", "Tournament", "Surface", "Partnering", "Opponents", "Score"], ["Runner-up", "8 November 1981", "$50,000", "Hong Kong", "Clay", "Susan Leo", "Ann Kiyomura\\n Sharon Walsh", "3–6, 4–6"], ["Runner-up", "29 January 1984", "$100,000", "Marco Island, United States", "Clay", "Andrea Jaeger", "Hana Mandlíková\\n Helena Suková", "6–3, 2–6, 2–6"], ["Runner-up", "16 April 1984", "$200,000", "Hilton Head, United States", "Clay", "Sharon Walsh", "Claudia Kohde-Kilsch\\n Hana Mandlíková", "5–7, 2–6"], ["Runner-up", "23 April 1984", "$200,000", "Orlando, United States", "Clay", "Wendy Turnbull", "Claudia Kohde-Kilsch\\n Hana Mandlíková", "0–6, 6–1, 3–6"], ["Runner-up", "10 December 1978", "$75,000", "Sydney, Australia", "Grass", "Judy Chaloner", "Kerry Reid\\n Wendy Turnbull", "2–6, 6–4, 2–6"], ["Runner-up", "20 May 1985", "$75,000", "Melbourne, Australia", "Carpet", "Kathy Jordan", "Pam Shriver\\n Liz Smylie", "2–6, 7–5, 1–6"], ["Runner-up", "30 August 1987", "$150,000", "Mahwah, United States", "Hard", "Liz Smylie", "Gigi Fernández\\n Lori McNeil", "3–6, 2–6"], ["Winner", "27 November 1983", "$150,000", "Sydney, Australia", "Grass", "Wendy Turnbull", "Hana Mandlíková\\n Helena Suková", "6–4, 6–3"], ["Runner-up", "19 June 1983", "$150,000", "Eastbourne, Great Britain", "Grass", "Jo Durie", "Martina Navrátilová\\n Pam Shriver", "1–6, 0–6"], ["Runner-up", "22 April 1984", "$250,000", "Amelia Island, United States", "Clay", "Mima Jaušovec", "Kathy Jordan\\n Anne Smith", "4–6, 6–3, 4–6"], ["Runner-up", "19 July 1987", "$150,000", "Newport, United States", "Grass", "Kathy Jordan", "Gigi Fernández\\n Lori McNeil", "6–7(5–7), 5–7"], ["Winner", "13 June 1982", "$100,000", "Birmingham, Great Britain", "Grass", "Jo Durie", "Rosie Casals\\n Wendy Turnbull", "6–3, 6–2"], ["Winner", "20 November 1983", "$150,000", "Brisbane, Australia", "Grass", "Wendy Turnbull", "Pam Shriver\\n Sharon Walsh", "6–3, 6–4"], ["Runner-up", "9 September 1984", "Grand Slam", "US Open, United States", "Hard", "Wendy Turnbull", "Martina Navrátilová\\n Pam Shriver", "2–6, 4–6"], ["Runner-up", "12 December 1983", "Grand Slam", "Australian Open, Australia", "Grass", "Wendy Turnbull", "Martina Navrátilová\\n Pam Shriver", "4–6, 7–6, 2–6"], ["Winner", "15 December 1985", "$50,000", "Auckland, New Zealand", "Grass", "Candy Reynolds", "Lea Antonoplis\\n Adriana Villagrán", "6–1, 6–3"], ["Winner", "20 May 1984", "$150,000", "Berlin, Germany", "Clay", "Candy Reynolds", "Kathy Horvath\\n Virginia Ruzici", "6–3, 4–6, 7–6(13–11)"], ["Winner", "23 May 1983", "$150,000", "Berlin, Germany", "Carpet", "Jo Durie", "Claudia Kohde-Kilsch\\n Eva Pfaff", "6–4, 7–6(7–2)"]]	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:susan leo served as a partner immediately after who?	Judy Chaloner	128	Answer:
Table InputTable: [["", "Chronological\\nNo.", "Date\\n(New style)", "Water level\\ncm", "Peak hour"], ["1", "84", "19 November 1824", "421", "14:00"], ["20", "55", "20 November 1764", "244", "–"], ["8", "14", "1 November 1726", "270", "–"], ["27", "177", "26 November 1898", "240", "23:30"], ["34", "171", "2 November 1895", "237", "3:00"], ["19", "144", "29 October 1874", "252", "4:00"], ["33", "145", "26 November 1874", "237", "4:00"], ["9", "183", "13 November 1903", "269", "9:00"], ["25", "175", "4 November 1897", "242", "12:00"], ["18", "83", "24 January 1822", "254", "night"], ["26", "261", "17 November 1974", "242", "1:00"], ["32", "76", "29 September 1788", "237", "–"], ["43", "208", "24 November 1922", "228", "19:15"], ["42", "125", "19 January 1866", "229", "10:00"], ["24", "136", "20 October 1873", "242", "–"], ["37", "41", "26 October 1752", "234", "12:00"], ["13", "319", "30 November 1999", "262", "4:35"], ["21", "201", "17 November 1917", "244", "6:50"], ["11", "86", "20 August 1831", "264", "night"], ["7", "9", "2 October 1723", "272", "–"], ["10", "7", "5 November 1721", "265", "daytime"], ["31", "18", "12 October 1729", "237", "10:00"], ["48", "122", "19 May 1865", "224", "9:10"], ["16", "215", "15 October 1929", "258", "17:15"], ["47", "81", "6 September 1802", "224", "daytime"], ["6", "39", "22 October 1752", "280", "10:00"], ["22", "254", "18 October 1967", "244", "13:30"], ["46", "211", "3 January 1925", "225", "21:30"], ["45", "116", "8 October 1863", "227", "2:00"], ["28", "260", "20 December 1973", "240", "7:15"], ["36", "37", "17 October 1744", "234", "–"], ["38", "43", "11 December 1752", "234", "night"], ["29", "219", "8 January 1932", "239", "3:00"], ["2", "210", "23 September 1924", "380", "19:15"], ["23", "45", "29 September 1756", "242", ""], ["14", "25", "10 September 1736", "261", ""], ["12", "3", "9 September 1706", "262", "daytime"], ["15", "298", "6 December 1986", "260", "13:30"], ["49", "202", "24 August 1918", "224", "9:10"], ["30", "225", "8 October 1935", "239", "5:50"], ["35", "227", "9 September 1937", "236", "5:30"], ["4", "244", "15 October 1955", "293", "20:45"], ["40", "269", "7 September 1977", "231", "16:50"], ["41", "292", "1 January 1984", "231", "21:20"], ["50", "242", "14 October 1954", "222", "21:00"], ["39", "228", "14 September 1938", "233", "2:25"], ["5", "264", "29 September 1975", "281", "4:00"], ["3", "71", "9 September 1777", "321", "morning"], ["44", "315", "12 October 1994", "228", "13:50"]]	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 water level on november 19 1824?	421	128	Answer:
Table InputTable: [["No", "Name", "Code", "Formed", "Headquarters", "Administrative\\nDivision", "Area (km2)", "Population\\n(2001 census)", "% of State\\nPopulation", "Density\\n(per km2)", "Urban (%)", "Literacy (%)", "Sex Ratio", "Tehsils", "Source"], ["32", "Thane", "TH", "1 May 1960", "Thane", "Konkan", "9,558", "8,131,849", "8.39%", "850.71", "72.58", "80.67", "858", "15", "District website"], ["22", "Nashik", "NS", "1 May 1960", "Nashik", "Nashik", "15,530", "4,993,796", "5.15%", "321.56", "38.8", "74.4", "927", "15", "District website"], ["28", "Sangli", "SN", "1 May 1960", "Sangli", "Pune", "8,578", "2,583,524", "2.67%", "301.18", "24.5", "62.41", "957", "10", "District website"], ["1", "Ahmednagar", "AH", "1 May 1960", "Ahmednagar", "Nashik", "17,413", "4,088,077", "4.22%", "234.77", "19.67", "75.82", "941", "14", "District website"], ["33", "Wardha", "WR", "1 May 1960", "Wardha", "Nagpur", "6,310", "1,230,640", "1.27%", "195.03", "25.17", "80.5", "936", "8", "District website"], ["5", "Beed", "BI", "1 May 1960", "Beed", "Aurangabad", "10,439", "2,161,250", "2.23%", "207.04", "17.91", "68", "936", "11", "District website"], ["3", "Amravati", "AM", "1 May 1960", "Amravati", "Amravati", "12,626", "2,606,063", "2.69%", "206.40", "34.50", "82.5", "938", "14", "District website"], ["31", "Solapur", "SO", "1 May 1960", "Daund", "Pune", "14,845", "3,849,543", "3.97%", "259.32", "31.8", "71.2", "935", "11", "District website"], ["20", "Nanded", "ND", "1 May 1960", "Nanded", "Aurangabad", "10,422", "2,876,259", "2.97%", "275.98", "28.29", "68.52", "942", "16", "District website"], ["34", "Washim", "WS", "1 July 1998", "Washim", "Amravati", "5,150", "1,020,216", "1.05%", "275.98", "17.49", "74.02", "939", "6", "District website"], ["25", "Pune", "PU", "1 May 1960", "Pune", "Pune", "15,642", "7,224,224", "7.46%", "461.85", "58.1", "80.78", "919", "14", "District website"], ["12", "Hingoli", "HI", "1 May 1999", "Hingoli", "Aurangabad", "4,526", "987,160", "1.02%", "218.11", "15.2", "66.86", "953", "5", "District website"], ["35", "Yavatmal", "YA", "1 May 1960", "Yavatmal", "Amravati", "13,582", "2,077,144", "2.14%", "152.93", "18.6", "57.96", "951", "16", "District website"], ["9", "Dhule", "DH", "1 May 1960", "Dhule", "Nashik", "8,063", "1,707,947", "1.76%", "211.83", "26.11", "71.6", "944", "4", "District website"]]	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:most district names are the same as their ...?	Headquarters	128	Answer:
Table InputTable: [["Date", "Date", "", "Rank", "Tournament name", "Venue", "City", "Winner", "Runner-up", "Score"], ["05–??", "05–??", "WAL", "", "Pontins Professional", "Pontins", "Prestatyn", "Martin Clark", "Andy Hicks", "9–7"], ["01–02", "01–05", "ENG", "", "Charity Challenge", "International Convention Centre", "Birmingham", "Stephen Hendry", "Ronnie O'Sullivan", "9–8"], ["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"], ["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"], ["02–13", "02–22", "SCO", "WR", "International Open", "A.E.C.C.", "Aberdeen", "Stephen Hendry", "Tony Drago", "9–1"], ["01–24", "02–01", "WAL", "WR", "Welsh Open", "Newport Leisure Centre", "Newport", "Stephen Hendry", "Mark King", "9–2"], ["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"], ["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"], ["03–18", "03–23", "IRL", "", "Irish Masters", "Goff's", "Kill", "Stephen Hendry", "Darren Morgan", "9–8"], ["12–09", "12–15", "GER", "WR", "German Open", "NAAFI", "Osnabrück", "Ronnie O'Sullivan", "Alain Robidoux", "9–7"], ["10–08", "10–13", "MLT", "", "Malta Grand Prix", "Jerma Palace Hotel", "Marsaskala", "Nigel Bond", "Tony Drago", "7–3"], ["12–28", "05–18", "ENG", "", "European League", "Diamond Centre", "Irthlingborough", "Ronnie O'Sullivan", "Stephen Hendry", "10–8"], ["09–24", "09–29", "SCO", "", "Scottish Masters", "Civic Centre", "Motherwell", "Peter Ebdon", "Alan McManus", "9–6"], ["10–16", "10–27", "ENG", "WR", "Grand Prix", "Bournemouth International Centre", "Bournemouth", "Mark Williams", "Euan Henderson", "9–5"]]	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:how many different venues are there in all?	19	128	Answer:
Table InputTable: [["Rank", "Player", "From", "Transfer Fee\\n(€ millions)", "Year"], ["2.", "Cesc Fàbregas", "Arsenal", "29+5(variables)", "2011"], ["1.", "Neymar", "Santos FC", "86.0", "2013"], ["3.", "Alexis Sánchez", "Udinese", "26+11(add ons)", "2011"], ["4.", "Javier Mascherano", "Liverpool", "26.8", "2010"], ["7.", "Adriano", "Sevilla", "13.5", "2010"], ["5.", "Alex Song", "Arsenal", "19.0", "2012"], ["6.", "Jordi Alba", "Valencia", "14.0", "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 players are in the table?	7	128	Answer:
Table InputTable: [["Pos", "Team", "Played", "Won", "Draw", "Lost", "Goals For", "Goals Against", "Goal Difference", "Points", "Notes"], ["4", "ÍBV", "18", "8", "5", "5", "29", "17", "+12", "29", "Inter-Toto Cup"], ["2", "Fylkir", "18", "10", "5", "3", "39", "16", "+23", "35", "UEFA Cup"], ["1", "KR", "18", "11", "4", "3", "27", "14", "+13", "37", "UEFA Champions League"], ["3", "Grindavík", "18", "8", "6", "4", "25", "18", "+7", "30", "UEFA Cup"], ["10", "Leiftur", "18", "3", "7", "8", "24", "39", "-15", "16", "Relegated"], ["9", "Stjarnan", "18", "4", "5", "9", "18", "31", "-13", "17", "Relegated"], ["7", "Breiðablik", "18", "5", "3", "10", "29", "35", "-6", "18", ""], ["6", "Keflavík", "18", "4", "7", "7", "21", "35", "-14", "19", ""], ["8", "Fram", "18", "4", "5", "9", "22", "33", "-11", "17", ""], ["5", "ÍA", "18", "7", "5", "6", "21", "17", "+4", "26", ""]]	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:only 2 teams had 7 draws, who were they?	Keflavík, Leiftur	128	Answer:
Table InputTable: [["Year", "Date", "Winner", "Result", "Loser", "Location"], ["2000", "September 10", "New York Giants", "33-18", "Philadelphia Eagles", "Veterans Stadium"], ["2000", "October 29", "New York Giants", "24-7", "Philadelphia Eagles", "Giants Stadium"], ["2002", "October 28", "Philadelphia Eagles", "17-3", "New York Giants", "Veterans Stadium"], ["2007", "September 30", "New York Giants", "16-3", "Philadelphia Eagles", "Giants Stadium"], ["2001", "October 22", "Philadelphia Eagles", "10-9", "New York Giants", "Giants Stadium"], ["2003", "October 19", "Philadelphia Eagles", "14-10", "New York Giants", "Giants Stadium"], ["2001", "December 30", "Philadelphia Eagles", "24-21", "New York Giants", "Veterans Stadium"], ["2004", "November 28", "Philadelphia Eagles", "27-6", "New York Giants", "Giants Stadium"], ["2005", "November 20", "New York Giants", "27-17", "Philadelphia Eagles", "Giants Stadium"], ["2001", "January 7", "New York Giants", "20-10", "Philadelphia Eagles", "Giants Stadium"], ["2006", "September 17", "New York Giants", "30-24 (OT)", "Philadelphia Eagles", "Lincoln Financial Field"], ["2009", "January 11", "Philadelphia Eagles", "23-11", "New York Giants", "Giants Stadium"], ["2002", "December 28", "New York Giants", "10-7", "Philadelphia Eagles", "Giants Stadium"], ["2004", "September 12", "Philadelphia Eagles", "31-17", "New York Giants", "Lincoln Financial Field"], ["2003", "November 16", "Philadelphia Eagles", "28-10", "New York Giants", "Lincoln Financial Field"], ["2006", "December 17", "Philadelphia Eagles", "36-22", "New York Giants", "Giants Stadium"], ["2009", "December 13", "Philadelphia Eagles", "45-38", "New York Giants", "Giants Stadium"], ["2008", "November 9", "New York Giants", "36-31", "Philadelphia Eagles", "Lincoln Financial Field"], ["2008", "December 7", "Philadelphia Eagles", "20-14", "New York Giants", "Giants Stadium"], ["2009", "November 1", "Philadelphia Eagles", "40-17", "New York Giants", "Lincoln Financial Field"], ["2005", "December 11", "New York Giants", "26-23 (OT)", "Philadelphia Eagles", "Lincoln Financial Field"], ["2007", "December 9", "New York Giants", "16-13", "Philadelphia Eagles", "Lincoln Financial Field"], ["2007", "January 7", "Philadelphia Eagles", "23-20", "New York Giants", "Lincoln Financial Field"]]	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 next location after the vetrans stadium game on september 10, 2000?	Giants Stadium	128	Answer:
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["2011", "Pan Arab Games", "Doha, Qatar", "5th", "100 m", "21.59"], ["2011", "World Championships", "Daegu, South Korea", "48th (h)", "200 m", "21.45"], ["2009", "World Championships", "Berlin, Germany", "25th (qf)", "200 m", "20.97"], ["2009", "Asian Championships", "Guangzhou, China", "1st", "200 m", "21.07"], ["2010", "Asian Games", "Guangzhou, China", "3rd", "200 m", "20.83"], ["2011", "Pan Arab Games", "Doha, Qatar", "3rd", "4x100 m", "40.15"], ["2008", "Olympic Games", "Beijing, China", "40th (h)", "200 m", "21.00"], ["2008", "Asian Indoor Championships", "Doha, Qatar", "4th", "60 m", "6.81"], ["2008", "World Indoor Championships", "Valencia, Spain", "28th (h)", "60 m", "6.88"], ["2008", "World Junior Championships", "Bydgoszcz, Poland", "7th", "200 m", "21.10"], ["2007", "Pan Arab Games", "Cairo, Egypt", "4th", "200 m", "20.94 (NR)"], ["2011", "Asian Championships", "Kobe, Jpan", "3rd", "200 m", "20.97"], ["2009", "Asian Indoor Games", "Hanoi, Vietnam", "4th", "60 m", "6.72 (NR)"]]	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 only competition with a 100m event?	Pan Arab Games	128	Answer:
Table InputTable: [["Rank", "Pair", "Country", "Athletes", "Time", "Deficit"], ["", "3", "Netherlands", "Sven Kramer\\nKoen Verweij\\nJan Blokhuijsen", "3:41.43", ""], ["", "2", "Russia", "Ivan Skobrev\\nDenis Yuskov\\nYevgeny Lalenkov", "3:43.62", "+2.19"], ["7", "4", "South Korea", "Lee Seung-hoon\\nJoo Hyong-jun\\nKo Byung-wook", "3:47.18", "+5.75"], ["8", "2", "Poland", "Zbigniew Bródka\\nKonrad Niedźwiedzki\\nJan Szymański", "3:47.72", "+6.29"], ["6", "3", "Germany", "Patrick Beckert\\nMarco Weber\\nRobert Lehmann", "3:46.48", "+5.05"], ["5", "1", "Norway", "Sverre Lunde Pedersen\\nHåvard Bøkko\\nKristian Reistad Fredriksen", "3:46.33", "+4.90"], ["4", "1", "Canada", "Denny Morrison\\nMathieu Giroux\\nLucas Makowsky", "3:44.38", "+2.95"], ["", "4", "United States", "Shani Davis\\nBrian Hansen\\nJonathan Kuck", "3:43.42", "+1.99"]]	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 countries competed in the event?	8	128	Answer:
Table InputTable: [["Model", "Fuel Type", "mpg (US gallons)", "L/100 km", "NZ Rating\\n(Stars)"], ["Citroen C4 2.0 SX 5DR 6SP A D", "diesel", "37.3", "6.3", "4.5"], ["Mini Cooper HATCH 6M 2DR 1.5L Diesel", "diesel", "53", "4.4", "5.5"], ["Volkswagen Jetta TDI 103KW 6DSG", "diesel", "37.9", "6.2", "4.5"], ["Kia Rio 1.5 DIESEL SEDAN MAN", "diesel", "52", "4.5", "5.5"], ["Volkswagen Golf TDI 103KW 6DSG", "diesel", "38.5", "6.1", "4.5"], ["Volkswagen Passat 2.0 TDI DSG VARIANT", "diesel", "37.9", "6.2", "4.5"], ["Volkswagen Touran 103KW TDI 6DSG", "diesel", "34.6", "6.8", "4.5"], ["Volkswagen Caddy LIFE 1.9 TDI MAN", "diesel", "37.9", "6.2", "4.5"], ["Citroen C4 1.6 HDI 6A EGS 5DR", "diesel", "52", "4.5", "5.5"], ["Citroen C3 1.6 HDI 5DR 5SPD", "diesel", "48", "4.9", "5"], ["Toyota RAV4 2.2D WAGON 6M L1", "diesel", "35.6", "6.6", "4.5"], ["Kia Rio 1.5 DIESEL HATCH MAN", "diesel", "52", "4.5", "5.5"], ["Volkswagen Caddy LIFE 1.9 TDI DSG", "diesel", "38.5", "6.1", "4.5"], ["Volkswagen Golf TDI 103KW 4MOTION", "diesel", "37.3", "6.3", "4.5"], ["Ford Focus WAG 1.6 MAN", "petrol", "35", "6.7", "4.5"], ["Volkswagen Crosstouran 103KW TDI 6DSG", "diesel", "34.6", "6.8", "4.5"], ["Volkswagen Passat TDI 125KW 6DSG VAR", "diesel", "35.6", "6.6", "4.5"], ["BMW 330D SEDAN 6M 4DR 3.0L", "diesel", "36.2", "6.5", "4.5"], ["Citroen C4 1.6 SX 5DR 5SP M D", "diesel", "50", "4.7", "5"], ["Kia Cerato 1.6 DIESEL 5M SEDAN", "diesel", "48", "4.9", "5"], ["Ford Focus 1.8TD WAGON", "diesel", "44.3", "5.3", "5"], ["BMW 535D SEDAN 6A 4D 3.0L", "diesel", "34.6", "6.8", "4.5"], ["Volkswagen Golf 90KW TSI 7DSG", "petrol", "39.8", "5.9", "5"], ["Mini Cooper COUPE 6A 3DR 1.6L Diesel", "diesel", "43.5", "5.4", "5"], ["Mini Cooper COUPE 6M 3DR 1.6L Diesel", "diesel", "52", "4.5", "5.5"], ["Volkswagen Golf 1.9 TDI 6DSG", "diesel", "39.2", "6", "5"], ["Volkswagen Golf 1.9 TDI 7DSG", "diesel", "44.3", "5.3", "5"], ["Volkswagen Golf 2.0 TDI 4 MOTION MAN", "diesel", "39.2", "6", "5"], ["Volkswagen Jetta 1.9 TDI 7DSG", "diesel", "51", "4.6", "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:what model has the top mpg (us gallons)?	Volkswagen Polo 1.4 TDI BLUEMOTION	128	Answer:
Table InputTable: [["Nr.", "Name", "Area (km²)", "Population (2006)", "Capital", "Club(s)"], ["3", "Cairo", "3,435", "7,786,640", "Cairo", "Al-Ahly - Al Mokawloon - ENPPI - El-Jaish - El-Shorta - Itesalat"], ["4", "Gharbia", "25,400", "3,790,670", "Tanta", "Ghazl El-Mehalla"], ["1", "Alexandria", "2,900", "4,110,015", "Alexandria", "Al Itthad Al Sakandary - El-Olympi - Haras El Hodood"], ["6", "Suez", "17,840", "510,935", "Suez", "Petrojet"], ["5", "Giza", "85,153", "6,272,571", "Giza", "Zamalek- Tersana"], ["6", "Ismailia", "1,442", "942,832", "Ismailia", "Ismaily"], ["7", "Port Said", "72", "570,768", "Port Said", "Al Masry"], ["2", "Asyut", "25,926", "3,441,597", "Asyut", "Petrol Asyout"]]	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 governorate has the most clubs?	Cairo	128	Answer:
Table InputTable: [["Week", "Date", "Opponent", "Result", "Game site", "NFL Recap", "Attendance"], ["9", "November 2, 1986", "at New York Giants", "L 14–17", "Giants Stadium", "[9]", "74,871"], ["1", "September 8, 1986", "New York Giants", "W 31–28", "Texas Stadium", "[1]", "59,804"], ["13", "November 27, 1986", "Seattle Seahawks", "L 14–31", "Texas Stadium", "[13]", "58,020"], ["10", "November 9, 1986", "Los Angeles Raiders", "L 13–17", "Texas Stadium", "[10]", "61,706"], ["15", "December 14, 1986", "Philadelphia Eagles", "L 21–23", "Texas Stadium", "[15]", "46,117"], ["7", "October 19, 1986", "at Philadelphia Eagles", "W 17–14", "Veterans Stadium", "[7]", "68,572"], ["3", "September 21, 1986", "Atlanta Falcons", "L 35–37", "Texas Stadium", "[3]", "62,880"], ["5", "October 5, 1986", "at Denver Broncos", "L 14–29", "Mile High Stadium", "[5]", "76,082"], ["8", "October 26, 1986", "St. Louis Cardinals", "W 37–6", "Texas Stadium", "[8]", "60,756"], ["16", "December 21, 1986", "Chicago Bears", "L 10–24", "Texas Stadium", "[16]", "57,256"], ["11", "November 16, 1986", "at San Diego Chargers", "W 24–21", "Jack Murphy Stadium", "[11]", "55,622"], ["12", "November 23, 1986", "at Washington Redskins", "L 14–41", "RFK Stadium", "[12]", "55,642"], ["6", "October 12, 1986", "Washington Redskins", "W 30–6", "Texas Stadium", "[6]", "63,264"], ["2", "September 14, 1986", "at Detroit Lions", "W 31–7", "Pontiac Silverdome", "[2]", "73,812"], ["14", "December 7, 1986", "at Los Angeles Rams", "L 10–29", "Anaheim Stadium", "[14]", "64,949"], ["4", "September 29, 1986", "at St. Louis Cardinals", "W 31–7", "Busch Memorial Stadium", "[4]", "49,077"]]	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 earned more points during week 9: dallas or new york?	New York	128	Answer:
Table InputTable: [["Association", "Joining year", "Men's team", "Women's team", "League"], ["Macau", "2002", "Macau", "Macau", "Campeonato da 1ª Divisão do Futebol"], ["Korea DPR", "2002", "Korea DPR", "Korea DPR", "DPR Korea League"], ["Hong Kong", "2002", "Hong Kong", "Hong Kong", "Hong Kong First Division League"], ["Japan", "2002", "Japan", "Japan", "J. League"], ["Guam", "2002", "Guam", "Guam", "Guam League"], ["Korea Republic", "2002", "Korea Republic", "Korea Republic", "K-League"], ["Chinese Taipei", "2002", "Chinese Taipei", "Chinese Taipei", "Intercity Football League"], ["China PR", "2002", "China", "China", "Chinese Super League"], ["Mongolia", "2002", "Mongolia", "Mongolia", "Mongolia Premier League"], ["Northern Mariana Islands", "2008 1", "Northern Mariana Islands", "Northern Mariana Islands", "Northern Mariana Championship"]]	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 associations joined in the year 2002?	9	128	Answer:
Table InputTable: [["Place", "Rider", "Country", "Team", "Points", "Wins"], ["16", "Gary Jones", "United States", "Yamaha", "439", "0"], ["12", "Andy Roberton", "United Kingdom", "Husqvarna", "810", "0"], ["8", "Gaston Rahier", "Belgium", "ČZ", "1112", "0"], ["17", "John DeSoto", "United States", "Suzuki", "425", "0"], ["1", "Sylvain Geboers", "Belgium", "Suzuki", "3066", "3"], ["14", "Mark Blackwell", "United States", "Husqvarna", "604", "0"], ["7", "Willy Bauer", "Germany", "Maico", "1276", "0"], ["5", "Joel Robert", "Belgium", "Suzuki", "1730", "1"], ["4", "Roger De Coster", "Belgium", "Suzuki", "1865", "3"], ["18", "Chris Horsefield", "United Kingdom", "ČZ", "416", "0"], ["3", "Torlief Hansen", "Sweden", "Husqvarna", "2052", "0"], ["9", "Pierre Karsmakers", "Netherlands", "Husqvarna", "1110", "0"], ["20", "Peter Lamppu", "United States", "Montesa", "309", "0"], ["19", "Uno Palm", "Sweden", "Husqvarna", "324", "0"], ["15", "Brad Lackey", "United States", "ČZ", "603", "0"], ["10", "Dave Bickers", "United Kingdom", "ČZ", "1076", "0"], ["11", "John Banks", "United Kingdom", "ČZ", "971", "0"], ["13", "Vlastimil Valek", "Czechoslovakia", "ČZ", "709", "0"], ["2", "Adolf Weil", "Germany", "Maico", "2331", "2"], ["6", "Heikki Mikkola", "Finland", "Husqvarna", "1680", "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:who was the top rider in the trans-ama final standings?	Sylvain Geboers	128	Answer:
Table InputTable: [["Type", "Construction period", "Cylinder", "Capacity", "Power", "Vmax"], ["Greif V8", "1934–1937", "V8", "2.489 cc", "55 PS (40 kW)", "110 km/h (68 mph)"], ["Greif V8 Sport", "1935–1937", "V8", "2.489 cc", "57 PS (42 kW)", "120 km/h (75 mph)"], ["10 PS (7 kW; 10 hp)", "1901–1902", "straight-2", "1.527 cc", "18 PS (13,2 kW)", "50 km/h (31 mph)"], ["Gigant G 15 K (15/80 PS)", "1928–1933", "straight-8", "3.974 cc", "80 PS (59 kW)", "110 km/h (68 mph)"], ["P2 (9/12 PS)", "1906–1907", "straight-2", "2.281 cc", "16 PS (11,8 kW)", "55 km/h (34 mph)"], ["Sedina", "1937–1940", "straight-4", "2.406 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)"], ["Arkona", "1937–1940", "straight-6", "3.610 cc", "80 PS (59 kW)", "120 km/h (75 mph)–140 km/h (87 mph)"], ["D2 (6/18 PS)", "1919–1920", "straight-4", "1.593 cc", "18 PS (13,2 kW)", "70 km/h (43 mph)"], ["Marschall M 12 (12/60 PS)", "1930–1934", "straight-8", "2.963 cc", "60 PS (44 kW)", "90 km/h (56 mph)"], ["D12 (12/45 PS)", "1923–1924", "straight-6", "3.107 cc", "45 PS (33 kW)", "100 km/h (62 mph)"], ["C2 (10/28 PS)", "1913–1914", "straight-4", "2.412 cc", "28 PS (20,6 kW)", "75 km/h (47 mph)"], ["D12V (13/55 PS)", "1925–1928", "straight-6", "3.386 cc", "55 PS (40 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)"], ["D9V (9/32 PS)", "1925–1927", "straight-4", "2.290 cc", "32 PS (23,5 kW)", "90 km/h (56 mph)"], ["8/14 PS", "1902–1905", "straight-2", "1.527 cc", "14 PS (10,3 kW)", "50 km/h (31 mph)"], ["B6 (9/22 PS)", "1912–1914", "straight-4", "4.900 cc", "45 PS (33 kW)", "95 km/h (59 mph)"], ["D9 (8/32 PS)", "1923–1924", "straight-4", "2.290 cc", "32 PS (23,5 kW)", "90 km/h (56 mph)"], ["20 PS (15 kW; 20 hp)", "1904–1905", "straight-4", "7.946 cc", "45 PS (33 kW)", "85 km/h (53 mph)"], ["D7 (42/120 PS)", "1919–1921", "straight-6", "11.160 cc", "120 PS (88 kW)", "160 km/h (99 mph)"], ["F6 (6/30 PS)", "1927–1928", "straight-4", "1.570 cc", "30 PS (22 kW)", "70 km/h (43 mph)"], ["C1 (6/18 PS)", "1909–1915", "straight-4", "1.546 cc", "18 PS (13,2 kW)", "70 km/h (43 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 passenger car models had a straight-2 cylinder engine?	3	128	Answer:
Table InputTable: [["Vessel", "Captor", "Date", "Location"], ["Susan", "Perry", "6 February 1849", "Rio de Janeiro"], ["Independence", "Perry", "13 December 1848", "Rio de Janeiro"], ["A.D. Richardson", "Perry", "11 December 1848", "Rio de Janeiro"], ["Laurens", "Onkahye", "23 January 1848", "Rio de Janeiro"], ["Porpoise", "Raritan", "23 January 1845", "Rio de Janeiro"], ["Albert", "Bainbridge", "June 1845", "Bahia"]]	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 vessels were captured in rio de janeiro	5	128	Answer:
Table InputTable: [["Parish", "Church name", "Location", "Year built"], ["Levanger", "Levanger Church", "Levanger", "1902"], ["Alstadhaug", "Alstadhaug Church", "Alstadhaug", "1180"], ["Markabygd", "Markabygda Church", "Markabygd", "1887"], ["Åsen", "Åsen Church", "Åsen", "1904"], ["Levanger", "Bamberg Church", "Levanger", "1998"], ["Ekne", "Ekne Church", "Ekne", "1893"], ["Ytterøy", "Ytterøy Church", "Ytterøya", "1890"], ["Okkenhaug", "Okkenhaug Chapel", "Okkenhaug", "1893"]]	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 parish had more than one church built.	Levanger	128	Answer:
Table InputTable: [["Pos", "Rider", "Manufacturer", "Time/Retired", "Points"], ["Ret", "Andre Romein", "Honda", "Retirement", ""], ["Ret", "Maurice Bolwerk", "TSR-Honda", "Retirement", ""], ["2", "Valentino Rossi", "Aprilia", "+0.180", "20"], ["24", "Henk Van De Lagemaat", "Honda", "+1 Lap", ""], ["Ret", "Marcellino Lucchi", "Aprilia", "Retirement", ""], ["Ret", "Roberto Rolfo", "Aprilia", "Retirement", ""], ["18", "Julien Allemand", "TSR-Honda", "+1:16.347", ""], ["16", "Luca Boscoscuro", "TSR-Honda", "+56.432", ""], ["8", "Stefano Perugini", "Honda", "+20.891", "8"], ["17", "Johann Stigefelt", "Yamaha", "+1:07.433", ""], ["20", "Lucas Oliver Bulto", "Yamaha", "+1:25.758", ""], ["1", "Loris Capirossi", "Honda", "38:04.730", "25"], ["11", "Alex Hofmann", "TSR-Honda", "+26.933", "5"], ["9", "Jason Vincent", "Honda", "+21.310", "7"], ["15", "Jarno Janssen", "TSR-Honda", "+56.248", "1"], ["21", "David Garcia", "Yamaha", "+1:33.867", ""], ["10", "Anthony West", "TSR-Honda", "+26.816", "6"], ["19", "Fonsi Nieto", "Yamaha", "+1:25.622", ""], ["7", "Franco Battaini", "Aprilia", "+20.889", "9"], ["22", "Rudie Markink", "Aprilia", "+1:40.280", ""], ["12", "Sebastian Porto", "Yamaha", "+27.054", "4"], ["13", "Tomomi Manako", "Yamaha", "+27.903", "3"], ["14", "Masaki Tokudome", "TSR-Honda", "+33.161", "2"], ["5", "Shinya Nakano", "Yamaha", "+0.742", "11"], ["3", "Jeremy McWilliams", "Aprilia", "+0.534", "16"], ["4", "Tohru Ukawa", "Honda", "+0.537", "13"], ["23", "Arno Visscher", "Aprilia", "+1:40.635", ""], ["6", "Ralf Waldmann", "Aprilia", "+7.019", "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 name of the rider that finished last?	Henk Van De Lagemaat	128	Answer:
Table InputTable: [["Character", "Real name", "Home world", "Membership notes", "Powers"], ["Dragonwing", "Marya Pai", "Earth", "First appeared in Legion of Super-Heroes vol. 6, #6 (December 2010) as a student at the Legion Academy.\\nJoined between Legion of Super-Heroes vol. 6, #16 (October 2011) and Legion of Super-Heroes vol. 7, #1 (November 2011).", "Fire breath and acid absorption."], ["Chameleon Girl", "Yera Allon", "Durla", "Pre-Crisis version first appeared (impersonating Shrinking Violet) in Legion of Super-Heroes vol. 2, #286 (April 1982).\\nTrue form and identity revealed in Legion of Super-Heroes vol. 2, #305 (November 1983).\\nLegion membership first revealed in Action Comics #861 (March 2008).", "Shapeshifting."], ["Night Girl", "Lydda Jath", "Kathoon", "Pre-Crisis version first appeared in Adventure Comics #306 (March 1963).\\nLegion membership first mentioned by Starman in Justice Society of America vol. 3, #6 (July 2007) and confirmed in Action Comics #860 (February 2008).", "Super-strength when not in direct sunlight."], ["Comet Queen", "Grava", "Extal Colony", "Pre-Crisis version first appeared in Legion of Super-Heroes vol. 2, #304 (October 1983) as a student at the Legion Academy.\\nJoined between Legion of Super-Heroes vol. 6, #16 (October 2011) and Legion of Super-Heroes vol. 7, #1 (November 2011).", "Space flight, comet gas extrusion."], ["Glorith II", "Glorith", "Unknown", "First appeared in Adventure Comics #523 (April 2011) as a student at the Legion Academy.\\nJoined between Legion of Super-Heroes vol. 6, #16 (October 2011) and Legion of Super-Heroes vol. 7, #1 (November 2011).", "Manipulation of mystical energies."], ["Chemical Kid", "Hadru Jamik", "Phlon", "First appeared in Legion of Super-Heroes vol. 6, #6 (December 2010) as a student at the Legion Academy.\\nJoined between Legion of Super-Heroes vol. 6, #16 (October 2011) and Legion of Super-Heroes vol. 7, #1 (November 2011).", "Catalyze chemical reactions."], ["Earth-Man", "Kirt Niedrigh", "Earth", "Pre-Crisis version first appeared (as \"Absorbency Boy\") in Superboy and the Legion of Super-Heroes #218 (July 1976).\\nJoined in Legion of Super-Heroes vol. 6, #2 (August 2010).\\nDied battling the Adversary in Legion of Super-Heroes vol. 6, #16 (October 2011).", "Super-power absorption and duplication."], ["Gates", "Ti'julk Mr'asz", "Vyrga", "First appeared in Legion of Super-Heroes vol. 4, #66 (March 1995).\\nJoined the Earth-247 team in Legion of Super-Heroes vol. 4, #76 (January 1996).\\nJoined the post-Infinite Crisis team in Final Crisis: Legion of 3 Worlds #5 (September 2009).", "Creation of teleportation \"gates\"."], ["XS", "Jenni Ognats", "Aarok", "First appeared in Legionnaires #0 (October 1994); granddaughter of Barry Allen and first cousin of Bart Allen.\\nNative of the same universe as the post-Infinite Crisis team, as revealed in Final Crisis: Legion of 3 Worlds #3 (April 2009).\\nJoined the Earth-247 team in Legion of Super-Heroes vol. 4, #62 (November 1994).\\nJoined the post-Infinite Crisis team in Final Crisis: Legion of 3 Worlds #5 (September 2009).\\nPost-Flashpoint no longer listed as a member of the Legion.", "Superspeed."], ["Karate Kid II", "Myg", "Lythyl", "Joined as a replacement for Val Armorr, as revealed in Final Crisis: Legion of 3 Worlds #1 (October 2008), unlike his counterpart who did not join the original team prior to Crisis on Infinite Earths.\\nKilled by Radiation Roy in Final Crisis: Legion of 3 Worlds #3 (April 2009).", "Mastery of all known martial arts."]]	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 pre-crisis superheros first appeared as students at the legion academy?	4	128	Answer:
Table InputTable: [["Name", "Owner", "Location", "Notes", "Transmission", "Website"], ["Radio Kiss Kiss", "", "Naples", "Commercial;", "FM, DVB-S", "http://www.kisskiss.it"], ["Multiradio", "Multiradio srl", "Massafra, (TA)", "Local; Adult Contemporary", "FM", "http://www.multiradio.it"], ["Radio Dimensione Suono", "", "Rome", "Commercial; It is also called RDS", "FM, DAB, DAB+, DVB-S", "http://www.rds.it"], ["RTL 102.5 Classic", "", "Milan", "Commercial; Classic hits", "DAB, DVB-S", "http://www.rtl.it"], ["RTL 102.5", "", "Cologno Monzese (MI)", "Commercial;", "FM, DAB, DVB-S", "http://www.rtl.it"], ["R101", "Monradio", "Milan", "Commercial; Classic hits", "FM, DAB, DAB+, DVB-S", "http://www.r101.it"], ["Radio 24", "Il Sole 24 Ore", "Milan", "Commercial; News/Talk", "FM, DAB, DVB-S", "http://www.radio24.it"], ["Radio Bruno", "Radio Bruno", "Carpi (MO)", "Local; Pop, Contemporary", "FM, streaming online, Dvb-T", "http://www.radiobruno.it"], ["m2o", "Elemedia", "Rome", "Commercial; Electronic dance music", "FM, DAB, DAB+, DVB-T, DVB-S", "http://www.m2o.it"], ["Rai Radio 3", "RAI", "Rome", "Public; Culture; Classical music", "FM, DAB, DVB-T, DVB-S", "http://www.radio3.rai.it"], ["Radio Popolare", "cooperative", "Rome", "Community; News/Talk", "FM", "http://www.radiopopolare.it"], ["Virgin Radio Italia", "Gruppo Finelco", "Milan", "Commercial; Rock", "FM, DAB, DAB+, DVB-S", "http://www.virginradioitaly.it"], ["Radio Maria", "Associazione Radio Maria", "Erba, (CO)", "Community; Catholic", "FM, DAB, DVB-S", "http://www.radiomaria.it"], ["Rai FD4 Leggera", "RAI", "Rome", "Public; Easy listening music", "DAB, Cable, DVB-T, DVB-S", "http://www.radio.rai.it/radiofd4"], ["Rai Radio 2", "RAI", "Rome", "Public; Popular music; Entertainment", "FM, DAB, DVB-T, DVB-S", "http://www.radio2.rai.it"], ["Rai Radio 1", "RAI", "Rome", "Public; News/Talk; Generalist", "FM, MW, DAB, DVB-T, DVB-S", "http://www.radio1.rai.it"], ["Rai Isoradio", "RAI", "", "Public; Traffic and weather news", "FM, DAB, DVB-S", "http://www.isoradio.rai.it"], ["Rai FD5 Auditorium", "RAI", "Rome", "Public; Classical music", "DAB, Cable, DVB-T, DVB-S", "http://www.radio.rai.it/radiofd5"], ["Radio DeeJay", "Elemedia", "Milan", "Commercial;", "FM, DAB, DAB+, DVB-T, DVB-S", "http://www.deejay.it"], ["Radio Capital", "Elemedia", "Cusano Milanino", "Commercial; Classic hits", "FM, DAB, DVB-T, DVB-S", "http://www.capital.it"], ["RadioRadio", "", "Rome", "Local; News/Talk", "DAB, DVB-S", "http://www.radioradio.it"], ["Radio Italia Solo Musica Italiana", "Gruppo Radio Italia", "Milan", "Commercial; Italian Hits", "FM, DAB, DVB-S", "http://www.radioitalia.it"], ["Radio Monte Carlo", "Gruppo Finelco", "Milan", "Commercial; It is also called RMC", "FM, DVB-S", "http://www.radiomontecarlo.net"], ["Radio Pianeta", "", "Cividate al piano. (BG)", "Local; News/Talk", "FM", "http://www.radiopianeta.it"]]	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 transmissions does radio kiss kiss have?	2	128	Answer:
Table InputTable: [["Year", "Song", "US Hot 100", "U.S. Modern Rock", "U.S. Mainstream Rock", "Album"], ["1997", "\"One Thing\"", "-", "-", "-", "Pacifier"], ["2001", "\"Bleeder\"", "-", "-", "32", "Violence"], ["1998", "\"Breathe Out\"", "-", "-", "-", "An Audio Guide To Everyday Atrocity"], ["1998", "\"The Sick\"", "-", "-", "-", "An Audio Guide To Everyday Atrocity"], ["2003", "\"Ether\"", "-", "-", "-", "Skeletons"], ["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:what single was top ranked?	Bleeder	128	Answer:
Table InputTable: [["Outcome", "No.", "Date", "Tournament", "Surface", "Partner", "Opponents", "Score"], ["Runner-up", "2.", "6 November 2011", "Taipei 5, Taiwan", "Hard", "Karolína Plíšková", "Chan Yung-jan\\n Zheng Jie", "6–7(5–7), 7–5, 3–6"], ["Runner-up", "1.", "16 May 2010", "Kurume, Japan", "Clay", "Karolína Plíšková", "Sun Shengnan\\n Xu Yifan", "0–6, 3–6"], ["Winner", "1.", "13 February 2011", "Rancho Mirage, United States", "Hard", "Karolína Plíšková", "Nadejda Guskova\\n Sandra Zaniewska", "6–7(6–8), 6–1, 6–4"], ["Winner", "2.", "7 August 2011", "Vancouver, Canada", "Hard", "Karolína Plíšková", "Jamie Hampton\\n N. Lertcheewakarn", "5–7, 6–2, 6–4"], ["Winner", "4.", "30 January 2012", "Grenoble, France", "Hard (i)", "Karolína Plíšková", "Valentyna Ivakhnenko\\n Maryna Zanevska", "6–1, 6–3"], ["Winner", "3.", "23 January 2012", "Andrézieux-Bouthéon, France", "Hard (i)", "Karolína Plíšková", "Julie Coin\\n Eva Hrdinová", "6–4, 4–6, [10–5]"], ["Runner-up", "4.", "17 September 2012", "Shrewsbury, United Kingdom", "Hard (i)", "Karolína Plíšková", "Vesna Dolonc\\n Stefanie Vögele", "1–6, 7–6(7–3), [13–15]"], ["Runner-up", "3.", "20 November 2011", "Bratislava, Slovakia", "Hard", "Karolína Plíšková", "Naomi Broady\\n Kristina Mladenovic", "7–5, 4–6, [2–10]"], ["Winner", "5.", "12 November 2012", "Zawada, Poland", "Carpet (i)", "Karolína Plíšková", "Kristina Barrois\\n Sandra Klemenschits", "6–3, 6–1"], ["Winner", "6.", "28 October 2013", "Barnstaple, United Kingdom", "Hard (i)", "Naomi Broady", "Raluca Olaru\\n Tamira Paszek", "6–3, 3–6, [10–5]"]]	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:where was the last tournament held?	Barnstaple, United Kingdom	128	Answer:
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["6", "South Korea", "0", "0", "2", "2"], ["5", "North Korea", "1", "0", "1", "2"], ["3", "Uzbekistan", "1", "2", "3", "6"], ["2", "Japan", "7", "10", "7", "24"], ["4", "Kazakhstan", "2", "2", "0", "4"], ["1", "China", "13", "9", "13", "35"], ["Total", "Total", "24", "23", "26", "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:which nation won the most silver medals?	Japan	128	Answer:
Table InputTable: [["Conference", "# of Bids", "Record", "Win %", "Round\\nof 32", "Sweet\\nSixteen", "Elite\\nEight", "Final\\nFour", "Championship\\nGame"], ["Western Athletic", "1", "1–1", ".500", "1", "–", "–", "–", "–"], ["Big East", "2", "5–2", ".714", "2", "2", "1", "–", "–"], ["Big Eight", "4", "3–4", ".429", "2", "1", "–", "–", "–"], ["West Coast", "2", "0–2", "–", "–", "–", "–", "–", "–"], ["Big Ten", "5", "9–5", ".643", "4", "2", "2", "1", "–"], ["Mid-Continent", "2", "0–2", "–", "–", "–", "–", "–", "–"], ["Atlantic Coast", "3", "9–2", ".818", "3", "2", "1", "1", "1"], ["Great Midwest", "2", "0–2", "–", "–", "–", "–", "–", "–"], ["Southeastern", "6", "10–6", ".625", "5", "3", "1", "1", "–"], ["Big West", "2", "0–2", "–", "–", "–", "–", "–", "–"], ["Sun Belt", "2", "6–2", ".750", "2", "1", "1", "1", "1"], ["Atlantic 10", "3", "1–3", ".250", "1", "–", "–", "–", "–"], ["Missouri Valley", "2", "2–2", ".500", "2", "–", "–", "–", "–"], ["Southwest", "4", "5–4", ".556", "3", "2", "–", "–", "–"], ["Pacific-10", "5", "8–5", ".615", "4", "2", "2", "–", "–"], ["Colonial", "1", "1–1", ".500", "1", "–", "–", "–", "–"], ["Metro", "2", "2–2", ".500", "1", "1", "–", "–", "–"], ["Big Sky", "2", "1–2", ".333", "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 most times a team went to the elite eight?	2	128	Answer:
Table InputTable: [["Rank", "Name", "Nationality", "Time"], ["8", "Edith Arraspide", "Argentina", "5:13.95"], ["", "Joanne Malar", "Canada", "4:43.64"], ["", "Jenny Kurth", "United States", "4:57.24"], ["", "Alison Fealey", "United States", "4:48.31"], ["5", "Fabíola Molina", "Brazil", "5:03.43"], ["7", "Carolyn Adel", "Suriname", "5:13.24"], ["6", "Isabel Rojas", "Colombia", "5:11.58"], ["4", "Sonia Fonseca", "Puerto Rico", "5:03.15"]]	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:what was the time difference between the gold and silver medal winners?	4.67	128	Answer:
Table InputTable: [["Place", "Code", "Area (km2)", "Population", "Most spoken language"], ["Remainder of the municipality", "91106", "2,944.04", "10,463", "Northern Sotho"], ["Manthata", "91105", "12.24", "22,121", "Northern Sotho"], ["Bochum", "91102", "11.64", "4,142", "Northern Sotho"], ["Dendron", "91103", "2.98", "1,885", "Northern Sotho"], ["Moletji", "91107", "11.66", "4,989", "Northern Sotho"], ["Soekmekaar", "91110", "1.06", "217", "Northern Sotho"], ["Ga-Ramokgopha", "91104", "11.22", "15,806", "Northern Sotho"], ["Backer", "91101", "0.34", "1,217", "Northern Sotho"], ["Sekhokho", "91109", "1.24", "1,852", "Northern Sotho"], ["Sekgosese", "91108", "349.99", "46,749", "Northern Sotho"]]	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 places have an area of more than 5?	5	128	Answer:
Table InputTable: [["Year", "Song", "Album", "Position", "Chart"], ["1989", "\"Sunshine\"", "24/7", "23", "Billboard Hot 100"], ["1993", "\"Endlessly\"", "The Way I Am", "--", "Billboard Hot 100"], ["1989", "\"I Like It\"", "24/7", "7", "Billboard Hot 100"], ["1989", "\"24/7\"", "24/7", "42", "Billboard Hot 100"], ["1990", "\"Romeo\"", "Swingin'", "6", "Billboard Hot 100"], ["1991", "\"Gentle\"", "Swingin'", "31", "Billboard Hot 100"], ["1993", "\"Ooh Child\"", "The Way I Am", "27", "Billboard Hot 100"], ["1989", "\"24/7\"", "24/7", "12", "Hot R&B/Hip-Hop Songs"], ["1990", "\"Never 2 Much of U\"", "24/7", "61", "Billboard Hot 100"], ["1989", "\"I Like It\"", "24/7", "3", "Hot Dance Club Play"], ["1987", "\"Summergirls\"", "24/7", "50", "Billboard Hot 100"]]	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 dino's singles appeared on charts besides the billboard hot 100?	2	128	Answer:
Table InputTable: [["Player", "No.", "Nationality", "Position", "Years for Jazz", "School/Club Team"], ["Terry Furlow", "25", "United States", "Guard/Forward", "1979-80", "Michigan State"], ["Bernie Fryer", "25", "United States", "Guard", "1975-76", "BYU"], ["Jim Farmer", "30", "United States", "Guard", "1988-89", "Alabama"], ["Greg Foster", "44", "United States", "Center/Forward", "1995-99", "UTEP"], ["Todd Fuller", "52", "United States", "Center", "1998-99", "North Carolina State"], ["Derrick Favors", "15", "United States", "Forward", "2011-present", "Georgia Tech"], ["Kyrylo Fesenko", "44", "Ukraine", "Center", "2007-11", "Cherkasy Monkeys (Ukraine)"], ["Derek Fisher", "2", "United States", "Guard", "2006-2007", "Arkansas-Little Rock"]]	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 players wore the number 25?	2	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 Felicia", "172,000", "210,000", "", "288,458", "261,127", "241,256", "148,028", "44,963", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−"], ["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"], ["Škoda Roomster", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "14,422", "66,661", "57,467", "47,152", "32,332", "36,000", "39,249", "33,300"], ["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 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 Rapid", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "1,700", "9,292", "103,800"], ["Škoda Citigo", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "509", "36,687", "45,200"], ["Škoda Yeti", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "−", "11,018", "52,604", "70,300", "90,952", "82,400"]]	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 different models were available for sale?	8	128	Answer:
Table InputTable: [["Hull", "Type", "Original Name", "Original Operator", "Delivery", "Disposition (2012)", "2nd name", "2nd operator", "3rd Name", "3rd operator"], ["№ 3", "929-117", "Toppy 1", "Tane Yaku Jetfoils", "Sep 1989", "Active", "Beetle 3", "JR Kyushu Jet Ferries", "", ""], ["№ 2", "929-117", "Pegasus", "Kyusyu Shosen Co. Ltd.", "Jun 1989", "Active", "Toppy 1", "Tane Yaku Jetfoils", "", ""], ["№ 13", "929-117", "Toppy 3", "Tane Yaku Jetfoils", "Mar 1995", "Active", "", "", "", ""], ["№ 12", "929-117", "Toppy 2", "Tane Yaku Jetfoils", "Apr 1992", "Active", "", "", "", ""], ["№ 6", "929-117", "Beetle", "JR Kyushu Jet Ferries", "Jul 1990", "Active", "Rocket", "Cosmo Line", "Rocket 3", "Tane Yaku Jetfoils"], ["№ 15", "929-117", "Emerald Wing", "Kaijo Access Co.", "Jun 1994", "Active", "2004 Rocket 1", "Cosmo Line", "-", "Tane Yaku Jetfoil"], ["№ 11", "929-117", "Princess Teguise", "Trasmediterranea", "Jun 1991", "Active", "2007 Toppy 5", "Tane Yaku Jetfoils", "", ""], ["№ 5", "929-117", "Nagasaki", "JR Kyushu Jet Ferries", "Apr 1990", "Active", "Beetle 1", "JR Kyushu Jet Ferries", "", ""], ["№ 8", "929-117", "Beetle 2", "JR Kyushu Jet Ferries", "Feb 1991", "Active", "", "", "", ""], ["№ 14", "929-117", "Crystal Wing", "Kaijo Access Co.", "Jun 1994", "Active", "2002 Beetle 5", "JR Kyushu Jet Ferries", "", ""], ["№ 7", "929-117", "Unicorn", "Kyusyu Shosen Co. Ltd.", "Oct 1990", "Active", "Pegasus 2", "Kyusyu Shosen Co. Ltd.", "", ""], ["№ 4", "929-117", "Princess Dacil", "Trasmediterranea", "Mar 1990", "Active", "Pegasus", "Kyusyu Shosen Co. Ltd.", "", ""], ["№ 9", "929-117", "Venus", "Kyushu Yusen", "Mar 1991", "Active", "", "", "", ""], ["№ 1", "929-117", "Tsubasa", "Sado Kisen", "Mar 1998", "Active", "", "", "", ""], ["№ 10", "929-117", "Suisei", "Sado Kisen", "Apr 1991", "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:how boats do tane yaku jetfoils operate in total?	3	128	Answer:
Table InputTable: [["Iteration", "Dates", "Location", "Attendance", "Notes"], ["GameStorm 15", "March 21–24, 2013", "Hilton - Vancouver, WA", "1188", ""], ["GameStorm 10", "March 2008", "Red Lion - Vancouver, WA", "750", "-"], ["GameStorm 13", "March 24–27, 2011", "Hilton - Vancouver, WA", "984", "Guests: Lisa Steenson, Michael A. Stackpole"], ["GameStorm 14", "March 22–25, 2012", "Hilton - Vancouver, WA", "1072", "Boardgame:Andrew Hackard and Sam Mitschke of Steve Jackson Games - RPG: Jason Bulmahn"], ["GameStorm 11", "March 26–29, 2009", "Hilton - Vancouver, WA", "736", "debut of Video games, first-ever Artist Guest of Honor, Rob Alexander"], ["GameStorm 12", "March 25–28, 2010", "Hilton - Vancouver, WA", "802", "Board games Guest of Honor: Tom Lehmann"], ["GameStorm 16", "March 20–23, 2014", "Hilton - Vancouver, WA", "tba", "Guests: Mike Selinker, Shane Lacy Hensley, Lisa Steenson, Zev Shlasinger of Z-Man Games"]]	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 gamestorms had at least 800 people?	4	128	Answer:
Table InputTable: [["Designation", "Keel Laid", "Launched", "Commissioned", "Fate"], ["PE-2", "10 May 1918", "19 August 1918", "11 July 1918", "Sold 11 June 1930"], ["PE-3", "16 May 1918", "11 September 1918", "11 November 1918", "Sold 11 June 1930"], ["PE-1", "7 May 1918", "11 July 1918", "27 October 1918", "Sold 11 June 1930"], ["PE-4", "21 May 1918", "15 September 1918", "14 November 1918", "Sold 11 June 1930"], ["PE-7", "8 June 1918", "5 October 1918", "24 November 1918", "Expended as target 30 November 1934"], ["PE-5", "28 May 1918", "28 September 1918", "19 November 1918", "Sold 11 June 1930"], ["PE-6", "3 June 1918", "16 October 1918", "21 November 1918", "Expended as target 30 November 1934"], ["PE-8", "10 June 1918", "11 November 1918", "31 October 1919", "Sold 1 April 1931"], ["PE-24", "13 September 1918", "24 February 1919", "12 July 1919", "Sold 11 June 1930"], ["PE-28", "23 October 1918", "1 March 1919", "28 July 1919", "Sold 11 June 1930"], ["PE-12", "13 July 1918", "12 November 1918", "6 November 1919", "Sold 30 December 1935"], ["PE-23", "11 September 1918", "20 February 1919", "19 June 1919", "Sold 11 June 1930"], ["PE-26", "25 September 1918", "1 March 1919", "1 October 1919", "Sold 29 August 1938"], ["PE-11", "13 July 1918", "14 November 1918", "29 May 1919", "Sold 16 January 1935"], ["PE-9", "17 June 1918", "8 November 1918", "27 October 1919", "Sold 26 May 1930"], ["PE-13", "15 July 1918", "9 January 1919", "2 April 1919", "Sold 26 May 1930"], ["PE-38", "30 January 1919", "29 March 1919", "30 July 1919", "In service during WWII\\nSold 3 March 1947"], ["PE-39", "3 February 1919", "29 March 1919", "20 September 1919", "Sold 7 June 1938"], ["PE-33", "14 February 1918", "15 March 1919", "4 September 1919", "Sold 11 June 1930"], ["PE-31", "19 November 1918", "8 March 1919", "14 August 1919", "Sold 18 May 1923"], ["PE-15", "21 July 1918", "25 January 1919", "11 June 1919", "Sold 14 June 1934"], ["PE-25", "17 September 1918", "19 February 1919", "30 June 1919", "Capsized in Delaware Bay squall 11 June 1920"], ["PE-34", "8 January 1919", "15 March 1919", "3 September 1919", "Sold 9 June 1932"], ["PE-19", "6 August 1918", "30 January 1919", "25 June 1919", "In service during WWII\\nDestroyed 6 August 1946"], ["PE-21", "31 August 1918", "15 February 1919", "31 July 1919", "Transferred to USCG late 1919"], ["PE-35", "13 January 1919", "22 March 1919", "22 August 1919", "Sold 7 June 1938"], ["PE-22", "5 September 1918", "10 February 1919", "17 July 1919", "Transferred to USCG late 1919"], ["PE-16", "22 July 1918", "11 January 1919", "5 June 1919", "Transferred to USCG late 1919"]]	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 in days between the first two "keel laid" dates?	3 days	128	Answer:
Table InputTable: [["Rank", "Athlete", "Nationality", "2.15", "2.20", "2.24", "2.27", "Result", "Notes"], ["1.", "Yaroslav Rybakov", "Russia", "o", "o", "o", "o", "2.27", "q"], ["1.", "Andriy Sokolovskyy", "Ukraine", "o", "o", "o", "o", "2.27", "q"], ["1.", "Andrey Tereshin", "Russia", "o", "o", "o", "o", "2.27", "q"], ["13.", "Tomáš Janku", "Czech Republic", "o", "o", "xxo", "xxx", "2.24", ""], ["15.", "Svatoslav Ton", "Czech Republic", "o", "xo", "xxx", "", "2.20", ""], ["1.", "Víctor Moya", "Cuba", "o", "o", "o", "o", "2.27", "q, PB"], ["1.", "Stefan Holm", "Sweden", "o", "o", "o", "o", "2.27", "q"], ["10.", "Nicola Ciotti", "Italy", "o", "o", "xo", "xxx", "2.24", ""], ["7.", "Giulio Ciotti", "Italy", "o", "o", "o", "xxx", "2.24", "q"], ["8.", "Robert Wolski", "Poland", "xo", "o", "o", "xxx", "2.24", "q"], ["16.", "Roman Fricke", "Germany", "o", "xxx", "", "2.15", "", ""], ["10.", "Wojciech Theiner", "Poland", "o", "o", "xo", "xxx", "2.24", ""], ["13.", "Mustapha Raifak", "France", "o", "o", "xxo", "xxx", "2.24", ""], ["1.", "Linus Thörnblad", "Sweden", "o", "o", "o", "o", "2.27", "q"], ["9.", "Ramsay Carelse", "South Africa", "xo", "xo", "o", "xxx", "2.24", ""], ["16.", "Adam Shunk", "United States", "o", "xxx", "", "2.15", "", ""], ["10.", "Tora Harris", "United States", "o", "o", "xo", "xxx", "2.24", ""]]	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 russian competitors qualified?	2	128	Answer:
Table InputTable: [["Represents", "Contestant", "Age", "Height", "Hometown"], ["Panamá Centro", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Chiriquí Occidente", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Chiriquí", "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"], ["Panamá Este", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Bocas del Toro", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Panamá Oeste", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Herrera", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Coclé", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Darién", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Colón", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Los Santos", "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:how many cities begin with the letter "c"?	5	128	Answer:
Table InputTable: [["Position", "Officer", "current officers", "superseded by", "Royal Household"], ["1", "Lord High Steward", "vacant", "Justiciar", "Lord Steward"], ["7", "Lord High Constable", "vacant", "Earl Marshal", "Master of the Horse"], ["2", "Lord High Chancellor", "The Rt Hon Chris Grayling, MP", "", ""], ["6", "Lord Great Chamberlain", "The Marquess of Cholmondeley", "Lord High Treasurer", "Lord Chamberlain"], ["3", "Lord High Treasurer", "in commission", "", ""], ["4", "Lord President of the Council", "The Rt Hon Nick Clegg, MP", "", ""], ["9", "Lord High Admiral", "HRH The Duke of Edinburgh", "", ""], ["5", "Lord Privy Seal", "The Rt Hon Andrew Lansley, CBE, MP", "", ""], ["8", "Earl Marshal", "The Duke of Norfolk", "", "Master of the Horse"]]	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:besides lord high steward, what other position is vacant?	Lord High Constable	128	Answer:
Table InputTable: [["Season", "Team", "Country", "Division", "Apps", "Goals"], ["2008", "Jiangsu Sainty", "China", "2", "24", "0"], ["2011", "Jiangsu Sainty", "China", "1", "9", "0"], ["2010", "Jiangsu Sainty", "China", "1", "17", "0"], ["2013", "Jiangsu Sainty", "China", "1", "11", "0"], ["2009", "Jiangsu Sainty", "China", "1", "15", "0"], ["2006", "Chengdu Wuniu", "China", "2", "1", "0"], ["2012", "Jiangsu Sainty", "China", "1", "0", "0"], ["2007", "Shandong Luneng", "China", "1", "0", "0"], ["2003", "Shandong Luneng", "China", "1", "4", "0"], ["2004", "Shandong Luneng", "China", "1", "0", "0"], ["2005", "Shandong Luneng", "China", "1", "0", "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 was the first game to score more than 10 apps?	2008	128	Answer:
Table InputTable: [["#", "Date", "Location", "Winner", "Score\\nJSU", "Score\\nTU", "Series"], ["8", "November 11, 1938", "Jacksonville, AL", "Tied", "6", "6", "TSU 4–3–1"], ["6", "November 10, 1933", "Jacksonville, AL", "Troy State", "7", "18", "Tied 3–3"], ["38", "November 11, 1972", "Jacksonville, AL", "Tied", "14", "14", "JSU 22–14–2"], ["21", "October 15, 1955", "Troy, AL", "Jacksonville State", "12", "0", "Tied 10–10–1"], ["17", "October 13, 1951", "Troy, AL", "Jacksonville State", "13", "7", "Tied 8–8–1"], ["", "Totals", "", "", "1086", "1110", "JSU 32–29–2"], ["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"], ["9", "November 11, 1939", "Troy, AL", "Troy State", "0", "27", "TSU 5–3–1"], ["14", "December 18, 1948", "Pensacola, FL", "Jacksonville State", "19", "0", "TSU 7–6–1"], ["2", "October 28, 1927", "?", "Jacksonville State", "26", "12", "JSU 2–0"], ["5", "November 12, 1932", "Montgomery, AL", "Troy State", "0", "20", "JSU 3–2"], ["48", "November 13, 1982", "Jacksonville, AL", "Jacksonville State", "49", "14", "JSU 29–17–2"], ["59", "November 22, 1997", "Troy, AL", "Troy State", "0", "49", "JSU 32–25–2"], ["4", "October 3, 1931", "Jacksonville, AL", "Troy State", "6", "24", "JSU 3–1"], ["54", "November 5, 1988", "Jacksonville, AL", "Jacksonville State", "31", "3", "JSU 30–22–2"], ["60", "November 21, 1998", "Jacksonville, AL", "Troy State", "7", "31", "JSU 32–26–2"], ["15", "October 15, 1949", "Troy, AL", "Troy State", "6", "27", "TSU 8–6–1"], ["50", "November 10, 1984", "Jacksonville, AL", "Troy State", "39", "42", "JSU 29–19–2"], ["20", "October 16, 1954", "Jacksonville, AL", "Jacksonville State", "38", "7", "TSU 10–9–1"], ["56", "November 3, 1990", "Jacksonville, AL", "Jacksonville State", "21", "10", "JSU 32–22–2"], ["62", "November 18, 2000", "Jacksonville, AL", "Troy State", "0", "28", "JSU 32–28–2"], ["49", "November 12, 1983", "Troy, AL", "Troy State", "3", "45", "JSU 29–18–2"], ["41", "November 15, 1975", "Troy, AL", "Troy State", "10", "26", "JSU 24–15–2"], ["3", "November 16, 1928", "Troy, AL", "Jacksonville State", "20", "0", "JSU 3–0"], ["13", "October 14, 1948", "Jacksonville, AL", "Jacksonville State", "25", "13", "TSU 7–5–1"], ["57", "October 21, 1995", "Troy, AL", "Troy State", "7", "35", "JSU 32–23–2"]]	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 are the total number of tied games listed?	2	128	Answer:
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["4", "Hungary", "1", "1", "3", "5"], ["1", "Netherlands", "8", "3", "1", "12"], ["7", "Russia", "–", "1", "1", "2"], ["6", "Italy", "–", "2", "1", "3"], ["8", "China", "–", "–", "1", "1"], ["2", "Australia", "3", "3", "4", "10"], ["5", "Canada", "1", "–", "3", "4"], ["3", "United States", "2", "5", "1", "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:which three nations received no gold medals?	Italy, Russia, China	128	Answer:
Table InputTable: [["District", "Incumbent", "2008 Status", "Democratic", "Republican"], ["1", "Diana DeGette", "Re-election", "Diana DeGette", "George Lilly"], ["4", "Marilyn Musgrave", "Re-election", "Betsy Markey", "Marilyn Musgrave"], ["7", "Ed Perlmutter", "Re-election", "Ed Perlmutter", "John W. Lerew"], ["3", "John Salazar", "Re-election", "John Salazar", "Wayne Wolf"], ["5", "Doug Lamborn", "Re-election", "Hal Bidlack", "Doug Lamborn"], ["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:what party does diana deguette belong to?	Democratic	128	Answer:
Table InputTable: [["Week", "Date", "Opponent", "Result", "Record", "TV Time", "Attendance"], ["2", "September 23, 2001", "at San Francisco 49ers", "W 30–26", "2–0", "FOX 3:15pm", "67,536"], ["13", "December 9, 2001", "San Francisco 49ers", "W 27–14", "10–2", "FOX 12:00pm", "66,218"], ["16", "December 30, 2001", "Indianapolis Colts", "W 42–17", "13–2", "CBS 12:00pm", "66,084"], ["3", "September 30, 2001", "Miami Dolphins", "W 42–10", "3–0", "CBS 12:00pm", "66,046"], ["1", "September 9, 2001", "at Philadelphia Eagles", "W 20–17 (OT)", "1–0", "FOX 3:15pm", "66,243"], ["17", "January 6, 2002", "Atlanta Falcons", "W 31–13", "14–2", "FOX 3:15pm", "66,033"], ["7", "October 28, 2001", "New Orleans Saints", "L 34–31", "6–1", "FOX 12:00pm", "66,189"], ["5", "October 14, 2001", "New York Giants", "W 15–14", "5–0", "FOX 12:00pm", "65,992"], ["4", "October 8, 2001", "at Detroit Lions", "W 35–0", "4–0", "ABC 8:00pm", "77,765"], ["15", "December 23, 2001", "at Carolina Panthers", "W 38–32", "12–2", "FOX 12:00pm", "72,438"], ["6", "October 21, 2001", "at New York Jets", "W 34–14", "6–0", "FOX 12:00pm", "78,766"], ["9", "November 11, 2001", "Carolina Panthers", "W 48–14", "7–1", "FOX 12:00pm", "66,069"], ["12", "December 2, 2001", "at Atlanta Falcons", "W 35–6", "9–2", "FOX 3:15pm", "60,787"], ["11", "November 26, 2001", "Tampa Bay Buccaneers", "L 24–17", "8–2", "ABC 8:00pm", "66,198"], ["10", "November 18, 2001", "at New England Patriots", "W 24–17", "8–1", "ESPN 7:30pm", "60,292"], ["14", "December 17, 2001", "at New Orleans Saints", "W 34–21", "11–2", "ABC 8:00pm", "70,332"], ["8", "Bye", "Bye", "Bye", "Bye", "Bye", "Bye"]]	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:against which team did the rams score the most points?	Carolina Panthers	128	Answer:
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["3", "West Germany (FRG)", "2", "0", "0", "2"], ["5", "Soviet Union (URS)", "0", "1", "2", "3"], ["6", "Sweden (SWE)", "0", "1", "1", "2"], ["4", "Norway (NOR)", "0", "2", "2", "4"], ["1", "Netherlands (NED)", "4", "3", "2", "9"], ["2", "United States (USA)", "2", "1", "1", "4"], ["", "Total", "8", "8", "8", "24"]]	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 most number of gold medals?	Netherlands (NED)	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."], ["Benteak", "Lagerstoemia parviflora", "", "675 kg/m³", "Kerala, Madras, Maharashtra, Karnataka", "It is strong and takes up a smooth surface. It may be used for building constructions, boat building and furniture."], ["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."], ["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."], ["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."], ["Siris", "Albizia spp.", "Dark brown", "", "North India", "Hard and durable, Siris wood is difficult to work. It is used for well curbs in salty water, beams, posts, and furniture."], ["Ironwood, Penaga Lilin,\\nBosneak, Gangaw, Mesua", "Mesua ferrea", "Reddish brown", "960–1060 kg/m³", "", "Ironwood is durable though it is very hard and is not easily worked. It even resists penetration of nails. It is used for ordinary house construction, bridges, piles, agricultural instruments, railway wagons, and railway sleepers."], ["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."], ["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."], ["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"], ["Spruce", "Picea spp.", "", "480 kg/m³", "", "Spruce wood resists decay and is not affected by the attack of marine borers. It is however liable to shrink, twist and warp. It is used for piles under water and (formerly) for aeroplane construction."], ["Red cedar", "", "Red", "480 kg/m³", "Assam, Nagpur", "It is soft and even grained. It is used for furniture, door panels and well curbs."], ["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."], ["Simul", "Bombax spp.", "White", "450 kg/m³", "All over India", "It is a loose grained, inferior quality wood. Light in weight, it is used for packing cases, the match industry, well curbs, and for cheap furniture."]]	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 each location listed for the benteak timber tree?	Kerala, Madras, Maharashtra, Karnataka	128	Answer:
Table InputTable: [["School", "2007", "2008", "2009", "2010", "2011"], ["Francisco Bravo Medical Magnet High School", "807", "818", "815", "820", "832"], ["Marc and Eva Stern Math and Science School", "718", "792", "788", "788", "809"], ["James A. Garfield High School", "553", "597", "593", "632", "705"], ["Woodrow Wilson High School", "582", "585", "600", "615", "636"], ["Santee Education Complex", "", "502", "521", "552", "565"], ["Oscar De La Hoya Animo Charter High School", "662", "726", "709", "710", "744"], ["Abraham Lincoln High School", "594", "609", "588", "616", "643"], ["Thomas Jefferson High School", "457", "516", "514", "546", "546"], ["Theodore Roosevelt High School", "557", "551", "576", "608", ""]]	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 only school to score higher than 800 in 2008?	Francisco Bravo Medical Magnet High School	128	Answer:

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