sail/symbolic-instruction-tuning huggingface dataset:
[Table] header: Birth | Marriage | Became Duchess | Ceased to be Duchess | Death | Spouse row 1 : 11 December 1455 | 4 June 1474 | 21 July 1495 Created Duchess | 24 February 1496 husband's death | 30 May 1503 | Eberhard I
row 2 : 29 October 1451 | April/May 1467 | 24 February 1496 husband's accession | 11 June 1498 husband's death | 28 March 1524 | Eberhard II
row 3 : 24 April 1492 | 2 March 1511 | 2 March 1511 | January 1519 husband's desposition | 30 August 1564 | Ulrich
row 4 : Württemberg annexed by Austria 1519 | Württemberg annexed by Austria 1519 | Württemberg annexed by Austria 1519 | Württemberg annexed by Austria 1519 | Württemberg annexed by Austria 1519 | Württemberg annexed by Austria 1519
row 5 : 24 April 1492 | 2 March 1511 | May 1534 husband's restoration | 6 November 1550 husband's death | 30 August 1564 | Ulrich
row 6 : 28 December 1526 | 24 February 1544 | 6 November 1550 husband's accession | 28 December 1568 husband's death | 20 May 1589 | Christoph
row 7 : 20 June 1559 | 7 November 1575 | 7 November 1575 | 19 May 1583 | 19 May 1583 | Louis III
row 8 : 24 February 1572 | 10 May 1585 | 10 May 1585 | 18 August 1593 husband's death | 15 February 1635 | Louis III
row 9 : 28 September 1564 | 22 May 1581 | 18 August 1593 husband's accession | 29 January 1608 husband's death | 26 October 1614 | Frederick I
row 10 : 16 November 1584 | 5 November 1609 | 5 November 1609 | 18 July 1628 husband's death | 13 February 1636 | John Frederick
row 11 : 27 January 1614 | 26 February 1637 | 26 February 1637 | 27 June 1655 | 27 June 1655 | Eberhard III
row 12 : 29 December 1639 | 20 July 1656 | 20 July 1656 | 2 July 1674 husband's death | 29 June 1698 | Eberhard III
row 13 : 28 April 1652 | 6 November 1673 | 2 July 1674 husband's accession | 23 June 1677 husband's death | 11 August 1712 | William Louis
row 14 : 3 October 1680 | 6/16 May 1697 | 6/16 May 1697 | 31 October 1733 husband's death | 2 July 1757 | Eberhard Louis
row 15 : 11 August 1706 | 1 May 1727 | 31 October 1733 husband's accession | 12 March 1737 husband's death | 1 February 1756 | Charles Alexander
row 16 : 30 August 1732 | 26 September 1748 | 26 September 1748 | 6 April 1780 | 6 April 1780 | Charles Eugene
row 17 : 18 December 1736 | 2 November 1753 | 20 May 1795 husband's accession | 23 December 1797 husband's death | 9 March 1798 | Frederick II Eugene
row 18 : 29 September 1766 | 18 May 1797 | 23 December 1797 husband's accession | 25 February 1803 elevated from Electress | 5 October 1828 | Frederick III [/Table] Answer this question based on the above table: WHo Became Duchess that has a Death of 26 october 1614?

Answer: 18 August 1593 husband's accession
[Table] header: Birth | Marriage | Became Duchess | Ceased to be Duchess | Death | Spouse row 1 : 11 December 1455 | 4 June 1474 | 21 July 1495 Created Duchess | 24 February 1496 husband's death | 30 May 1503 | Eberhard I
row 2 : 29 October 1451 | April/May 1467 | 24 February 1496 husband's accession | 11 June 1498 husband's death | 28 March 1524 | Eberhard II
row 3 : 24 April 1492 | 2 March 1511 | 2 March 1511 | January 1519 husband's desposition | 30 August 1564 | Ulrich
row 4 : Württemberg annexed by Austria 1519 | Württemberg annexed by Austria 1519 | Württemberg annexed by Austria 1519 | Württemberg annexed by Austria 1519 | Württemberg annexed by Austria 1519 | Württemberg annexed by Austria 1519
row 5 : 24 April 1492 | 2 March 1511 | May 1534 husband's restoration | 6 November 1550 husband's death | 30 August 1564 | Ulrich
row 6 : 28 December 1526 | 24 February 1544 | 6 November 1550 husband's accession | 28 December 1568 husband's death | 20 May 1589 | Christoph
row 7 : 20 June 1559 | 7 November 1575 | 7 November 1575 | 19 May 1583 | 19 May 1583 | Louis III
row 8 : 24 February 1572 | 10 May 1585 | 10 May 1585 | 18 August 1593 husband's death | 15 February 1635 | Louis III
row 9 : 28 September 1564 | 22 May 1581 | 18 August 1593 husband's accession | 29 January 1608 husband's death | 26 October 1614 | Frederick I
row 10 : 16 November 1584 | 5 November 1609 | 5 November 1609 | 18 July 1628 husband's death | 13 February 1636 | John Frederick
row 11 : 27 January 1614 | 26 February 1637 | 26 February 1637 | 27 June 1655 | 27 June 1655 | Eberhard III
row 12 : 29 December 1639 | 20 July 1656 | 20 July 1656 | 2 July 1674 husband's death | 29 June 1698 | Eberhard III
row 13 : 28 April 1652 | 6 November 1673 | 2 July 1674 husband's accession | 23 June 1677 husband's death | 11 August 1712 | William Louis
row 14 : 3 October 1680 | 6/16 May 1697 | 6/16 May 1697 | 31 October 1733 husband's death | 2 July 1757 | Eberhard Louis
row 15 : 11 August 1706 | 1 May 1727 | 31 October 1733 husband's accession | 12 March 1737 husband's death | 1 February 1756 | Charles Alexander
row 16 : 30 August 1732 | 26 September 1748 | 26 September 1748 | 6 April 1780 | 6 April 1780 | Charles Eugene
row 17 : 18 December 1736 | 2 November 1753 | 20 May 1795 husband's accession | 23 December 1797 husband's death | 9 March 1798 | Frederick II Eugene
row 18 : 29 September 1766 | 18 May 1797 | 23 December 1797 husband's accession | 25 February 1803 elevated from Electress | 5 October 1828 | Frederick III [/Table] Answer this question based on the above table: What is the Spouse of the Duchess that has a Death on 6 april 1780?

Answer: Charles Eugene
[Table] header: Driver | Points | Season | Races | Percentage of possible points row 1 : Sebastian Vettel | 392 | 2011 | 19 | 82.53
row 2 : Michael Schumacher | 380 | 2002 | 17 | 89.41
row 3 : Michael Schumacher | 367 | 2004 | 18 | 81.56
row 4 : Sebastian Vettel | 347 | 2013 * | 17 | 81.65
row 5 : Michael Schumacher | 327 | 2001 | 17 | 76.94
row 6 : Fernando Alonso | 322 | 2005 | 19 | 67.79
row 7 : Fernando Alonso | 321 | 2006 | 18 | 71.33
row 8 : Alain Prost | 301 | 1988 | 16 | 75.25
row 9 : Michael Schumacher | 296 | 2006 | 18 | 65.78
row 10 : Michael Schumacher | 286 | 2000 | 17 | 67.29 [/Table] Answer this question based on the above table: Which season with 16 races had less than 321 points and a larger percent than 65.78 possible?

Answer: 1988.0
[Table] header: Driver | Points | Season | Races | Percentage of possible points row 1 : Sebastian Vettel | 392 | 2011 | 19 | 82.53
row 2 : Michael Schumacher | 380 | 2002 | 17 | 89.41
row 3 : Michael Schumacher | 367 | 2004 | 18 | 81.56
row 4 : Sebastian Vettel | 347 | 2013 * | 17 | 81.65
row 5 : Michael Schumacher | 327 | 2001 | 17 | 76.94
row 6 : Fernando Alonso | 322 | 2005 | 19 | 67.79
row 7 : Fernando Alonso | 321 | 2006 | 18 | 71.33
row 8 : Alain Prost | 301 | 1988 | 16 | 75.25
row 9 : Michael Schumacher | 296 | 2006 | 18 | 65.78
row 10 : Michael Schumacher | 286 | 2000 | 17 | 67.29 [/Table] Answer this question based on the above table: What season had less than 301 points and 17 races?

Answer: 2000.0
[Table] header: Race | Trophy | Rider(s) | Machine | Year | Average speed | Time row 1 : Overall | Jimmy Simpson Trophy | John McGuinness | Honda 1000cc | 2013 | mph (km/h) | 17:11.572
row 2 : Senior TT | Norman Brown Trophy | Bruce Anstey | Honda 1000cc | 2013 | mph (km/h) | 17:12.671
row 3 : TT Superbike | John Williams Trophy | John McGuinness | Honda 1000cc | 2013 | mph (km/h) | 17:11.572
row 4 : TT Superstock | Don Ryder Trophy | Michael Dunlop | Honda CBR1000RR | 2013 | mph (km/h) | 17:15.114
row 5 : TT Supersport Race | Formula 2 TT Trophy | Michael Dunlop | Honda CBR600RR | 2013 | mph (km/h) | 17:35.659
row 6 : TT Sidecar Race | Jock Taylor Trophy | Ben Birchall/Tom Birchall | LCR 600cc | 2013 | mph (km/h) | 19:44.598
row 7 : Fastest Female | Susan Jenness Trophy | Jenny Tinmouth | Honda 1000cc | 2010 | mph (km/h) | 18:52.42 [/Table] Answer this question based on the above table: What time did bruce anstey complete in a honda 1000cc?

Answer: 17:12.671
[Table] header: Race | Trophy | Rider(s) | Machine | Year | Average speed | Time row 1 : Overall | Jimmy Simpson Trophy | John McGuinness | Honda 1000cc | 2013 | mph (km/h) | 17:11.572
row 2 : Senior TT | Norman Brown Trophy | Bruce Anstey | Honda 1000cc | 2013 | mph (km/h) | 17:12.671
row 3 : TT Superbike | John Williams Trophy | John McGuinness | Honda 1000cc | 2013 | mph (km/h) | 17:11.572
row 4 : TT Superstock | Don Ryder Trophy | Michael Dunlop | Honda CBR1000RR | 2013 | mph (km/h) | 17:15.114
row 5 : TT Supersport Race | Formula 2 TT Trophy | Michael Dunlop | Honda CBR600RR | 2013 | mph (km/h) | 17:35.659
row 6 : TT Sidecar Race | Jock Taylor Trophy | Ben Birchall/Tom Birchall | LCR 600cc | 2013 | mph (km/h) | 19:44.598
row 7 : Fastest Female | Susan Jenness Trophy | Jenny Tinmouth | Honda 1000cc | 2010 | mph (km/h) | 18:52.42 [/Table] Answer this question based on the above table: Who holds the time of 17:11.572?

Answer: Overall, TT Superbike
[Table] header: Race | Trophy | Rider(s) | Machine | Year | Average speed | Time row 1 : Overall | Jimmy Simpson Trophy | John McGuinness | Honda 1000cc | 2013 | mph (km/h) | 17:11.572
row 2 : Senior TT | Norman Brown Trophy | Bruce Anstey | Honda 1000cc | 2013 | mph (km/h) | 17:12.671
row 3 : TT Superbike | John Williams Trophy | John McGuinness | Honda 1000cc | 2013 | mph (km/h) | 17:11.572
row 4 : TT Superstock | Don Ryder Trophy | Michael Dunlop | Honda CBR1000RR | 2013 | mph (km/h) | 17:15.114
row 5 : TT Supersport Race | Formula 2 TT Trophy | Michael Dunlop | Honda CBR600RR | 2013 | mph (km/h) | 17:35.659
row 6 : TT Sidecar Race | Jock Taylor Trophy | Ben Birchall/Tom Birchall | LCR 600cc | 2013 | mph (km/h) | 19:44.598
row 7 : Fastest Female | Susan Jenness Trophy | Jenny Tinmouth | Honda 1000cc | 2010 | mph (km/h) | 18:52.42 [/Table] Answer this question based on the above table: What machine did susan jenness trophy earn the trophy?

Answer: Honda 1000cc
[Table] header: District | 2010 Population (000) | 2008 GDP (USD bn) a | 2008 GDP per capita (USD) a | Agri culture b | Mining b | Manufac turing b | Services & cons truction b | Exports (USD mn) 2011 | Median mo. salary (USD) a e | Vehicles (per 1000) d | Income poverty f | Structural poverty g row 1 : City of Buenos Aires | 2890 | 118.000 | 40,828 | 0.3 | 1.0 | 12.9 | 85.8 | 426 | 1,618 | 528 | 7.3 | 7.8
row 2 : Buenos Aires Province | 15625 | 161.000 | 10,303 | 4.5 | 0.1 | 21.3 | 74.1 | 28134 | 1,364 | 266 | 16.2 | 15.8
row 3 : Catamarca | 368 | 2.331 | 6,009 | 3.6 | 20.8 | 12.1 | 63.5 | 1596 | 1,241 | 162 | 24.3 | 21.5
row 4 : Chaco | 1055 | 2.120 | 2,015 | 12.6 | 0.0 | 7.5 | 79.9 | 602 | 1,061 | 137 | 35.4 | 33.0
row 5 : Chubut | 509 | 7.110 | 15,422 | 6.9 | 21.3 | 10.0 | 61.8 | 3148 | 2,281 | 400 | 4.6 | 15.5
row 6 : Córdoba | 3309 | 33.239 | 10,050 | 10.6 | 0.2 | 14.0 | 75.2 | 10635 | 1,200 | 328 | 14.8 | 13.0
row 7 : Corrientes | 993 | 4.053 | 4,001 | 12.6 | 0.0 | 8.2 | 79.2 | 230 | 1,019 | 168 | 31.5 | 28.5
row 8 : Entre Ríos | 1236 | 7.137 | 5,682 | 11.9 | 0.3 | 11.6 | 76.2 | 1908 | 1,063 | 280 | 13.0 | 17.6
row 9 : Formosa | 530 | 1.555 | 2,879 | 7.6 | 1.5 | 6.4 | 84.5 | 40 | 1,007 | 107 | 30.7 | 33.6
row 10 : Jujuy | 673 | 2.553 | 3,755 | 5.5 | 0.7 | 14.6 | 79.2 | 456 | 1,123 | 153 | 30.0 | 28.8
row 11 : La Pampa | 319 | 2.000 | 5,987 | 19.0 | 3.7 | 5.3 | 72.0 | 378 | 1,164 | 364 | 13.6 | 10.3
row 12 : La Rioja | 334 | 1.419 | 4,162 | 3.9 | 0.1 | 16.8 | 79.2 | 281 | 1,040 | 172 | 22.0 | 20.4
row 13 : Mendoza | 1739 | 18.800 | 10,758 | 5.4 | 6.1 | 17.5 | 71.0 | 1862 | 1,153 | 313 | 12.2 | 15.4
row 14 : Misiones | 1102 | 4.044 | 3,751 | 6.7 | 0.0 | 13.0 | 80.3 | 536 | 971 | 153 | 32.6 | 27.1
row 15 : Neuquén | 551 | 14.398 | 26,273 | 0.7 | 42.8 | 2.9 | 53.6 | 353 | 2,211 | 339 | 11.2 | 17.0
row 16 : Río Negro | 639 | 4.924 | 8,247 | 4.9 | 8.4 | 7.3 | 79.4 | 670 | 1,309 | 285 | 20.6 | 17.9
row 17 : Salta | 1214 | 5.165 | 4,220 | 8.1 | 7.6 | 10.4 | 73.9 | 1332 | 1,045 | 139 | 29.7 | 31.6
row 18 : San Juan | 681 | 3.927 | 5,642 | 8.1 | 0.3 | 15.9 | 75.7 | 2470 | 1,293 | 216 | 18.4 | 17.4
row 19 : San Luis | 432 | 2.444 | 5,580 | 4.9 | 0.5 | 42.4 | 52.2 | 735 | 1,288 | 245 | 22.0 | 15.6
row 20 : Santa Cruz | 274 | 6.892 | 30,496 | 4.4 | 47.1 | 2.3 | 46.2 | 1857 | 2,646 | 432 | 3.6 | 10.4
row 21 : Santa Fe | 3195 | 37.500 | 10,670 | 10.1 | 0.0 | 17.4 | 72.5 | 17804 | 1,265 | 299 | 18.2 | 14.8
row 22 : Santiago del Estero | 874 | 2.598 | 3,003 | 11.5 | 0.1 | 6.2 | 82.2 | 1082 | 945 | 103 | 31.0 | 31.3
row 23 : Tierra del Fuego | 127 | 2.606 | 20,682 | 4.7 | 18.5 | 18.6 | 58.2 | 443 | 2,267 | 478 | 6.4 | 14.1
row 24 : Tucumán | 1448 | 5.807 | 3,937 | 6.0 | 0.1 | 12.6 | 81.3 | 1031 | 973 | 146 | 27.7 | 23.9
row 25 : Argentina | 40117 | 328.696 | 8,269 | 5.0 | 2.7 | 16.3 | 76.0 | 83950 c | 1,301 | 284 | 17.8 | 17.7 [/Table] Answer this question based on the above table: 2008 GDP per capita (USD) is smaller than 20,682, and District is chubut, and a Structural poverty g larger than 15.5, what is the average manufac?

Answer: none
[Table] header: District | 2010 Population (000) | 2008 GDP (USD bn) a | 2008 GDP per capita (USD) a | Agri culture b | Mining b | Manufac turing b | Services & cons truction b | Exports (USD mn) 2011 | Median mo. salary (USD) a e | Vehicles (per 1000) d | Income poverty f | Structural poverty g row 1 : City of Buenos Aires | 2890 | 118.000 | 40,828 | 0.3 | 1.0 | 12.9 | 85.8 | 426 | 1,618 | 528 | 7.3 | 7.8
row 2 : Buenos Aires Province | 15625 | 161.000 | 10,303 | 4.5 | 0.1 | 21.3 | 74.1 | 28134 | 1,364 | 266 | 16.2 | 15.8
row 3 : Catamarca | 368 | 2.331 | 6,009 | 3.6 | 20.8 | 12.1 | 63.5 | 1596 | 1,241 | 162 | 24.3 | 21.5
row 4 : Chaco | 1055 | 2.120 | 2,015 | 12.6 | 0.0 | 7.5 | 79.9 | 602 | 1,061 | 137 | 35.4 | 33.0
row 5 : Chubut | 509 | 7.110 | 15,422 | 6.9 | 21.3 | 10.0 | 61.8 | 3148 | 2,281 | 400 | 4.6 | 15.5
row 6 : Córdoba | 3309 | 33.239 | 10,050 | 10.6 | 0.2 | 14.0 | 75.2 | 10635 | 1,200 | 328 | 14.8 | 13.0
row 7 : Corrientes | 993 | 4.053 | 4,001 | 12.6 | 0.0 | 8.2 | 79.2 | 230 | 1,019 | 168 | 31.5 | 28.5
row 8 : Entre Ríos | 1236 | 7.137 | 5,682 | 11.9 | 0.3 | 11.6 | 76.2 | 1908 | 1,063 | 280 | 13.0 | 17.6
row 9 : Formosa | 530 | 1.555 | 2,879 | 7.6 | 1.5 | 6.4 | 84.5 | 40 | 1,007 | 107 | 30.7 | 33.6
row 10 : Jujuy | 673 | 2.553 | 3,755 | 5.5 | 0.7 | 14.6 | 79.2 | 456 | 1,123 | 153 | 30.0 | 28.8
row 11 : La Pampa | 319 | 2.000 | 5,987 | 19.0 | 3.7 | 5.3 | 72.0 | 378 | 1,164 | 364 | 13.6 | 10.3
row 12 : La Rioja | 334 | 1.419 | 4,162 | 3.9 | 0.1 | 16.8 | 79.2 | 281 | 1,040 | 172 | 22.0 | 20.4
row 13 : Mendoza | 1739 | 18.800 | 10,758 | 5.4 | 6.1 | 17.5 | 71.0 | 1862 | 1,153 | 313 | 12.2 | 15.4
row 14 : Misiones | 1102 | 4.044 | 3,751 | 6.7 | 0.0 | 13.0 | 80.3 | 536 | 971 | 153 | 32.6 | 27.1
row 15 : Neuquén | 551 | 14.398 | 26,273 | 0.7 | 42.8 | 2.9 | 53.6 | 353 | 2,211 | 339 | 11.2 | 17.0
row 16 : Río Negro | 639 | 4.924 | 8,247 | 4.9 | 8.4 | 7.3 | 79.4 | 670 | 1,309 | 285 | 20.6 | 17.9
row 17 : Salta | 1214 | 5.165 | 4,220 | 8.1 | 7.6 | 10.4 | 73.9 | 1332 | 1,045 | 139 | 29.7 | 31.6
row 18 : San Juan | 681 | 3.927 | 5,642 | 8.1 | 0.3 | 15.9 | 75.7 | 2470 | 1,293 | 216 | 18.4 | 17.4
row 19 : San Luis | 432 | 2.444 | 5,580 | 4.9 | 0.5 | 42.4 | 52.2 | 735 | 1,288 | 245 | 22.0 | 15.6
row 20 : Santa Cruz | 274 | 6.892 | 30,496 | 4.4 | 47.1 | 2.3 | 46.2 | 1857 | 2,646 | 432 | 3.6 | 10.4
row 21 : Santa Fe | 3195 | 37.500 | 10,670 | 10.1 | 0.0 | 17.4 | 72.5 | 17804 | 1,265 | 299 | 18.2 | 14.8
row 22 : Santiago del Estero | 874 | 2.598 | 3,003 | 11.5 | 0.1 | 6.2 | 82.2 | 1082 | 945 | 103 | 31.0 | 31.3
row 23 : Tierra del Fuego | 127 | 2.606 | 20,682 | 4.7 | 18.5 | 18.6 | 58.2 | 443 | 2,267 | 478 | 6.4 | 14.1
row 24 : Tucumán | 1448 | 5.807 | 3,937 | 6.0 | 0.1 | 12.6 | 81.3 | 1031 | 973 | 146 | 27.7 | 23.9
row 25 : Argentina | 40117 | 328.696 | 8,269 | 5.0 | 2.7 | 16.3 | 76.0 | 83950 c | 1,301 | 284 | 17.8 | 17.7 [/Table] Answer this question based on the above table: Agri culture b larger than 12.6, what is the lowest vehicles per 1000?

Answer: 364.0
[Table] header: District | 2010 Population (000) | 2008 GDP (USD bn) a | 2008 GDP per capita (USD) a | Agri culture b | Mining b | Manufac turing b | Services & cons truction b | Exports (USD mn) 2011 | Median mo. salary (USD) a e | Vehicles (per 1000) d | Income poverty f | Structural poverty g row 1 : City of Buenos Aires | 2890 | 118.000 | 40,828 | 0.3 | 1.0 | 12.9 | 85.8 | 426 | 1,618 | 528 | 7.3 | 7.8
row 2 : Buenos Aires Province | 15625 | 161.000 | 10,303 | 4.5 | 0.1 | 21.3 | 74.1 | 28134 | 1,364 | 266 | 16.2 | 15.8
row 3 : Catamarca | 368 | 2.331 | 6,009 | 3.6 | 20.8 | 12.1 | 63.5 | 1596 | 1,241 | 162 | 24.3 | 21.5
row 4 : Chaco | 1055 | 2.120 | 2,015 | 12.6 | 0.0 | 7.5 | 79.9 | 602 | 1,061 | 137 | 35.4 | 33.0
row 5 : Chubut | 509 | 7.110 | 15,422 | 6.9 | 21.3 | 10.0 | 61.8 | 3148 | 2,281 | 400 | 4.6 | 15.5
row 6 : Córdoba | 3309 | 33.239 | 10,050 | 10.6 | 0.2 | 14.0 | 75.2 | 10635 | 1,200 | 328 | 14.8 | 13.0
row 7 : Corrientes | 993 | 4.053 | 4,001 | 12.6 | 0.0 | 8.2 | 79.2 | 230 | 1,019 | 168 | 31.5 | 28.5
row 8 : Entre Ríos | 1236 | 7.137 | 5,682 | 11.9 | 0.3 | 11.6 | 76.2 | 1908 | 1,063 | 280 | 13.0 | 17.6
row 9 : Formosa | 530 | 1.555 | 2,879 | 7.6 | 1.5 | 6.4 | 84.5 | 40 | 1,007 | 107 | 30.7 | 33.6
row 10 : Jujuy | 673 | 2.553 | 3,755 | 5.5 | 0.7 | 14.6 | 79.2 | 456 | 1,123 | 153 | 30.0 | 28.8
row 11 : La Pampa | 319 | 2.000 | 5,987 | 19.0 | 3.7 | 5.3 | 72.0 | 378 | 1,164 | 364 | 13.6 | 10.3
row 12 : La Rioja | 334 | 1.419 | 4,162 | 3.9 | 0.1 | 16.8 | 79.2 | 281 | 1,040 | 172 | 22.0 | 20.4
row 13 : Mendoza | 1739 | 18.800 | 10,758 | 5.4 | 6.1 | 17.5 | 71.0 | 1862 | 1,153 | 313 | 12.2 | 15.4
row 14 : Misiones | 1102 | 4.044 | 3,751 | 6.7 | 0.0 | 13.0 | 80.3 | 536 | 971 | 153 | 32.6 | 27.1
row 15 : Neuquén | 551 | 14.398 | 26,273 | 0.7 | 42.8 | 2.9 | 53.6 | 353 | 2,211 | 339 | 11.2 | 17.0
row 16 : Río Negro | 639 | 4.924 | 8,247 | 4.9 | 8.4 | 7.3 | 79.4 | 670 | 1,309 | 285 | 20.6 | 17.9
row 17 : Salta | 1214 | 5.165 | 4,220 | 8.1 | 7.6 | 10.4 | 73.9 | 1332 | 1,045 | 139 | 29.7 | 31.6
row 18 : San Juan | 681 | 3.927 | 5,642 | 8.1 | 0.3 | 15.9 | 75.7 | 2470 | 1,293 | 216 | 18.4 | 17.4
row 19 : San Luis | 432 | 2.444 | 5,580 | 4.9 | 0.5 | 42.4 | 52.2 | 735 | 1,288 | 245 | 22.0 | 15.6
row 20 : Santa Cruz | 274 | 6.892 | 30,496 | 4.4 | 47.1 | 2.3 | 46.2 | 1857 | 2,646 | 432 | 3.6 | 10.4
row 21 : Santa Fe | 3195 | 37.500 | 10,670 | 10.1 | 0.0 | 17.4 | 72.5 | 17804 | 1,265 | 299 | 18.2 | 14.8
row 22 : Santiago del Estero | 874 | 2.598 | 3,003 | 11.5 | 0.1 | 6.2 | 82.2 | 1082 | 945 | 103 | 31.0 | 31.3
row 23 : Tierra del Fuego | 127 | 2.606 | 20,682 | 4.7 | 18.5 | 18.6 | 58.2 | 443 | 2,267 | 478 | 6.4 | 14.1
row 24 : Tucumán | 1448 | 5.807 | 3,937 | 6.0 | 0.1 | 12.6 | 81.3 | 1031 | 973 | 146 | 27.7 | 23.9
row 25 : Argentina | 40117 | 328.696 | 8,269 | 5.0 | 2.7 | 16.3 | 76.0 | 83950 c | 1,301 | 284 | 17.8 | 17.7 [/Table] Answer this question based on the above table: Manufac turing b of 16.8, and a 2008 GDP per capita (USD) a smaller than 4,162, so what is the total number of mining b?

Answer: 0.0
[Table] header: District | 2010 Population (000) | 2008 GDP (USD bn) a | 2008 GDP per capita (USD) a | Agri culture b | Mining b | Manufac turing b | Services & cons truction b | Exports (USD mn) 2011 | Median mo. salary (USD) a e | Vehicles (per 1000) d | Income poverty f | Structural poverty g row 1 : City of Buenos Aires | 2890 | 118.000 | 40,828 | 0.3 | 1.0 | 12.9 | 85.8 | 426 | 1,618 | 528 | 7.3 | 7.8
row 2 : Buenos Aires Province | 15625 | 161.000 | 10,303 | 4.5 | 0.1 | 21.3 | 74.1 | 28134 | 1,364 | 266 | 16.2 | 15.8
row 3 : Catamarca | 368 | 2.331 | 6,009 | 3.6 | 20.8 | 12.1 | 63.5 | 1596 | 1,241 | 162 | 24.3 | 21.5
row 4 : Chaco | 1055 | 2.120 | 2,015 | 12.6 | 0.0 | 7.5 | 79.9 | 602 | 1,061 | 137 | 35.4 | 33.0
row 5 : Chubut | 509 | 7.110 | 15,422 | 6.9 | 21.3 | 10.0 | 61.8 | 3148 | 2,281 | 400 | 4.6 | 15.5
row 6 : Córdoba | 3309 | 33.239 | 10,050 | 10.6 | 0.2 | 14.0 | 75.2 | 10635 | 1,200 | 328 | 14.8 | 13.0
row 7 : Corrientes | 993 | 4.053 | 4,001 | 12.6 | 0.0 | 8.2 | 79.2 | 230 | 1,019 | 168 | 31.5 | 28.5
row 8 : Entre Ríos | 1236 | 7.137 | 5,682 | 11.9 | 0.3 | 11.6 | 76.2 | 1908 | 1,063 | 280 | 13.0 | 17.6
row 9 : Formosa | 530 | 1.555 | 2,879 | 7.6 | 1.5 | 6.4 | 84.5 | 40 | 1,007 | 107 | 30.7 | 33.6
row 10 : Jujuy | 673 | 2.553 | 3,755 | 5.5 | 0.7 | 14.6 | 79.2 | 456 | 1,123 | 153 | 30.0 | 28.8
row 11 : La Pampa | 319 | 2.000 | 5,987 | 19.0 | 3.7 | 5.3 | 72.0 | 378 | 1,164 | 364 | 13.6 | 10.3
row 12 : La Rioja | 334 | 1.419 | 4,162 | 3.9 | 0.1 | 16.8 | 79.2 | 281 | 1,040 | 172 | 22.0 | 20.4
row 13 : Mendoza | 1739 | 18.800 | 10,758 | 5.4 | 6.1 | 17.5 | 71.0 | 1862 | 1,153 | 313 | 12.2 | 15.4
row 14 : Misiones | 1102 | 4.044 | 3,751 | 6.7 | 0.0 | 13.0 | 80.3 | 536 | 971 | 153 | 32.6 | 27.1
row 15 : Neuquén | 551 | 14.398 | 26,273 | 0.7 | 42.8 | 2.9 | 53.6 | 353 | 2,211 | 339 | 11.2 | 17.0
row 16 : Río Negro | 639 | 4.924 | 8,247 | 4.9 | 8.4 | 7.3 | 79.4 | 670 | 1,309 | 285 | 20.6 | 17.9
row 17 : Salta | 1214 | 5.165 | 4,220 | 8.1 | 7.6 | 10.4 | 73.9 | 1332 | 1,045 | 139 | 29.7 | 31.6
row 18 : San Juan | 681 | 3.927 | 5,642 | 8.1 | 0.3 | 15.9 | 75.7 | 2470 | 1,293 | 216 | 18.4 | 17.4
row 19 : San Luis | 432 | 2.444 | 5,580 | 4.9 | 0.5 | 42.4 | 52.2 | 735 | 1,288 | 245 | 22.0 | 15.6
row 20 : Santa Cruz | 274 | 6.892 | 30,496 | 4.4 | 47.1 | 2.3 | 46.2 | 1857 | 2,646 | 432 | 3.6 | 10.4
row 21 : Santa Fe | 3195 | 37.500 | 10,670 | 10.1 | 0.0 | 17.4 | 72.5 | 17804 | 1,265 | 299 | 18.2 | 14.8
row 22 : Santiago del Estero | 874 | 2.598 | 3,003 | 11.5 | 0.1 | 6.2 | 82.2 | 1082 | 945 | 103 | 31.0 | 31.3
row 23 : Tierra del Fuego | 127 | 2.606 | 20,682 | 4.7 | 18.5 | 18.6 | 58.2 | 443 | 2,267 | 478 | 6.4 | 14.1
row 24 : Tucumán | 1448 | 5.807 | 3,937 | 6.0 | 0.1 | 12.6 | 81.3 | 1031 | 973 | 146 | 27.7 | 23.9
row 25 : Argentina | 40117 | 328.696 | 8,269 | 5.0 | 2.7 | 16.3 | 76.0 | 83950 c | 1,301 | 284 | 17.8 | 17.7 [/Table] Answer this question based on the above table: Manufac turing b larger than 10, and a 2008 GDP per capita (USD) a larger than 4,162, and a Median mo. salary (USD)of 1,301, and a 2010 Population (000) smaller than 40117 so what is the lowest services & construction b?

Answer: none
[Table] header: District | 2010 Population (000) | 2008 GDP (USD bn) a | 2008 GDP per capita (USD) a | Agri culture b | Mining b | Manufac turing b | Services & cons truction b | Exports (USD mn) 2011 | Median mo. salary (USD) a e | Vehicles (per 1000) d | Income poverty f | Structural poverty g row 1 : City of Buenos Aires | 2890 | 118.000 | 40,828 | 0.3 | 1.0 | 12.9 | 85.8 | 426 | 1,618 | 528 | 7.3 | 7.8
row 2 : Buenos Aires Province | 15625 | 161.000 | 10,303 | 4.5 | 0.1 | 21.3 | 74.1 | 28134 | 1,364 | 266 | 16.2 | 15.8
row 3 : Catamarca | 368 | 2.331 | 6,009 | 3.6 | 20.8 | 12.1 | 63.5 | 1596 | 1,241 | 162 | 24.3 | 21.5
row 4 : Chaco | 1055 | 2.120 | 2,015 | 12.6 | 0.0 | 7.5 | 79.9 | 602 | 1,061 | 137 | 35.4 | 33.0
row 5 : Chubut | 509 | 7.110 | 15,422 | 6.9 | 21.3 | 10.0 | 61.8 | 3148 | 2,281 | 400 | 4.6 | 15.5
row 6 : Córdoba | 3309 | 33.239 | 10,050 | 10.6 | 0.2 | 14.0 | 75.2 | 10635 | 1,200 | 328 | 14.8 | 13.0
row 7 : Corrientes | 993 | 4.053 | 4,001 | 12.6 | 0.0 | 8.2 | 79.2 | 230 | 1,019 | 168 | 31.5 | 28.5
row 8 : Entre Ríos | 1236 | 7.137 | 5,682 | 11.9 | 0.3 | 11.6 | 76.2 | 1908 | 1,063 | 280 | 13.0 | 17.6
row 9 : Formosa | 530 | 1.555 | 2,879 | 7.6 | 1.5 | 6.4 | 84.5 | 40 | 1,007 | 107 | 30.7 | 33.6
row 10 : Jujuy | 673 | 2.553 | 3,755 | 5.5 | 0.7 | 14.6 | 79.2 | 456 | 1,123 | 153 | 30.0 | 28.8
row 11 : La Pampa | 319 | 2.000 | 5,987 | 19.0 | 3.7 | 5.3 | 72.0 | 378 | 1,164 | 364 | 13.6 | 10.3
row 12 : La Rioja | 334 | 1.419 | 4,162 | 3.9 | 0.1 | 16.8 | 79.2 | 281 | 1,040 | 172 | 22.0 | 20.4
row 13 : Mendoza | 1739 | 18.800 | 10,758 | 5.4 | 6.1 | 17.5 | 71.0 | 1862 | 1,153 | 313 | 12.2 | 15.4
row 14 : Misiones | 1102 | 4.044 | 3,751 | 6.7 | 0.0 | 13.0 | 80.3 | 536 | 971 | 153 | 32.6 | 27.1
row 15 : Neuquén | 551 | 14.398 | 26,273 | 0.7 | 42.8 | 2.9 | 53.6 | 353 | 2,211 | 339 | 11.2 | 17.0
row 16 : Río Negro | 639 | 4.924 | 8,247 | 4.9 | 8.4 | 7.3 | 79.4 | 670 | 1,309 | 285 | 20.6 | 17.9
row 17 : Salta | 1214 | 5.165 | 4,220 | 8.1 | 7.6 | 10.4 | 73.9 | 1332 | 1,045 | 139 | 29.7 | 31.6
row 18 : San Juan | 681 | 3.927 | 5,642 | 8.1 | 0.3 | 15.9 | 75.7 | 2470 | 1,293 | 216 | 18.4 | 17.4
row 19 : San Luis | 432 | 2.444 | 5,580 | 4.9 | 0.5 | 42.4 | 52.2 | 735 | 1,288 | 245 | 22.0 | 15.6
row 20 : Santa Cruz | 274 | 6.892 | 30,496 | 4.4 | 47.1 | 2.3 | 46.2 | 1857 | 2,646 | 432 | 3.6 | 10.4
row 21 : Santa Fe | 3195 | 37.500 | 10,670 | 10.1 | 0.0 | 17.4 | 72.5 | 17804 | 1,265 | 299 | 18.2 | 14.8
row 22 : Santiago del Estero | 874 | 2.598 | 3,003 | 11.5 | 0.1 | 6.2 | 82.2 | 1082 | 945 | 103 | 31.0 | 31.3
row 23 : Tierra del Fuego | 127 | 2.606 | 20,682 | 4.7 | 18.5 | 18.6 | 58.2 | 443 | 2,267 | 478 | 6.4 | 14.1
row 24 : Tucumán | 1448 | 5.807 | 3,937 | 6.0 | 0.1 | 12.6 | 81.3 | 1031 | 973 | 146 | 27.7 | 23.9
row 25 : Argentina | 40117 | 328.696 | 8,269 | 5.0 | 2.7 | 16.3 | 76.0 | 83950 c | 1,301 | 284 | 17.8 | 17.7 [/Table] Answer this question based on the above table: Income poverty f smaller than 13.6, and a Mining b of 1, and a Structural poverty g smaller than 7.8, so what is the total number of agriculture?

Answer: 0.0
[Table] header: English name | Bulgarian name | Bulgarian name ( Transliteration ) | Old Bulgarian Names | Old Bulgarian name (Transliteration) | Old Bulgarian name - Meaning row 1 : January | Януари | Yanuari | Просинец, Кол | Prosinets, Kolog, Kol | Month of burning trunks
row 2 : February | Февруари | Fevruari | Съчен, Сечко, | Sachen, Sechko, Malak se | Month of dreadful ice
row 3 : March | Март | Mart | Сух | Suh | Dry month
row 4 : April | Април | April | Брязок | Bryazok | Colorful month
row 5 : May | Май | May | Тръвен | Traven | Grass month
row 6 : June | Юни | Yuni | Изок, Червени | Izok, Chervenik | Red month
row 7 : July | Юли | Yuli | Чръвенъ, ж | Chraven, Zhetar, Zhat | Harvesting month
row 8 : August | Август | Avgust | Орач, Зарев | Orach, Zarev | Tillage month
row 9 : September | Септември | Septemvri | Руен, Руй | Ruen, Ruy | Bounteous month
row 10 : October | Октомври | Oktomvri | Листопад | Listopad | Month of falling leaves
row 11 : November | Ноември | Noemvri | Груден | Gruden | Month of tubers
row 12 : December | Декември | Dekemvri | Студен | Studen | Cold month [/Table] Answer this question based on the above table: What's the bulgarian word for January?

Answer: Януари
[Table] header: English name | Bulgarian name | Bulgarian name ( Transliteration ) | Old Bulgarian Names | Old Bulgarian name (Transliteration) | Old Bulgarian name - Meaning row 1 : January | Януари | Yanuari | Просинец, Кол | Prosinets, Kolog, Kol | Month of burning trunks
row 2 : February | Февруари | Fevruari | Съчен, Сечко, | Sachen, Sechko, Malak se | Month of dreadful ice
row 3 : March | Март | Mart | Сух | Suh | Dry month
row 4 : April | Април | April | Брязок | Bryazok | Colorful month
row 5 : May | Май | May | Тръвен | Traven | Grass month
row 6 : June | Юни | Yuni | Изок, Червени | Izok, Chervenik | Red month
row 7 : July | Юли | Yuli | Чръвенъ, ж | Chraven, Zhetar, Zhat | Harvesting month
row 8 : August | Август | Avgust | Орач, Зарев | Orach, Zarev | Tillage month
row 9 : September | Септември | Septemvri | Руен, Руй | Ruen, Ruy | Bounteous month
row 10 : October | Октомври | Oktomvri | Листопад | Listopad | Month of falling leaves
row 11 : November | Ноември | Noemvri | Груден | Gruden | Month of tubers
row 12 : December | Декември | Dekemvri | Студен | Studen | Cold month [/Table] Answer this question based on the above table: What's the old bulgarian word for тръвен?

Answer: Grass month
[Table] header: English name | Bulgarian name | Bulgarian name ( Transliteration ) | Old Bulgarian Names | Old Bulgarian name (Transliteration) | Old Bulgarian name - Meaning row 1 : January | Януари | Yanuari | Просинец, Кол | Prosinets, Kolog, Kol | Month of burning trunks
row 2 : February | Февруари | Fevruari | Съчен, Сечко, | Sachen, Sechko, Malak se | Month of dreadful ice
row 3 : March | Март | Mart | Сух | Suh | Dry month
row 4 : April | Април | April | Брязок | Bryazok | Colorful month
row 5 : May | Май | May | Тръвен | Traven | Grass month
row 6 : June | Юни | Yuni | Изок, Червени | Izok, Chervenik | Red month
row 7 : July | Юли | Yuli | Чръвенъ, ж | Chraven, Zhetar, Zhat | Harvesting month
row 8 : August | Август | Avgust | Орач, Зарев | Orach, Zarev | Tillage month
row 9 : September | Септември | Septemvri | Руен, Руй | Ruen, Ruy | Bounteous month
row 10 : October | Октомври | Oktomvri | Листопад | Listopad | Month of falling leaves
row 11 : November | Ноември | Noemvri | Груден | Gruden | Month of tubers
row 12 : December | Декември | Dekemvri | Студен | Studen | Cold month [/Table] Answer this question based on the above table: What's the old Bulgarian word for avgust?

Answer: Tillage month
[Table] header: English name | Bulgarian name | Bulgarian name ( Transliteration ) | Old Bulgarian Names | Old Bulgarian name (Transliteration) | Old Bulgarian name - Meaning row 1 : January | Януари | Yanuari | Просинец, Кол | Prosinets, Kolog, Kol | Month of burning trunks
row 2 : February | Февруари | Fevruari | Съчен, Сечко, | Sachen, Sechko, Malak se | Month of dreadful ice
row 3 : March | Март | Mart | Сух | Suh | Dry month
row 4 : April | Април | April | Брязок | Bryazok | Colorful month
row 5 : May | Май | May | Тръвен | Traven | Grass month
row 6 : June | Юни | Yuni | Изок, Червени | Izok, Chervenik | Red month
row 7 : July | Юли | Yuli | Чръвенъ, ж | Chraven, Zhetar, Zhat | Harvesting month
row 8 : August | Август | Avgust | Орач, Зарев | Orach, Zarev | Tillage month
row 9 : September | Септември | Septemvri | Руен, Руй | Ruen, Ruy | Bounteous month
row 10 : October | Октомври | Oktomvri | Листопад | Listopad | Month of falling leaves
row 11 : November | Ноември | Noemvri | Груден | Gruden | Month of tubers
row 12 : December | Декември | Dekemvri | Студен | Studen | Cold month [/Table] Answer this question based on the above table: What's the old bulgarian word for октомври?

Answer: Month of falling leaves
[Table] header: English name | Bulgarian name | Bulgarian name ( Transliteration ) | Old Bulgarian Names | Old Bulgarian name (Transliteration) | Old Bulgarian name - Meaning row 1 : January | Януари | Yanuari | Просинец, Кол | Prosinets, Kolog, Kol | Month of burning trunks
row 2 : February | Февруари | Fevruari | Съчен, Сечко, | Sachen, Sechko, Malak se | Month of dreadful ice
row 3 : March | Март | Mart | Сух | Suh | Dry month
row 4 : April | Април | April | Брязок | Bryazok | Colorful month
row 5 : May | Май | May | Тръвен | Traven | Grass month
row 6 : June | Юни | Yuni | Изок, Червени | Izok, Chervenik | Red month
row 7 : July | Юли | Yuli | Чръвенъ, ж | Chraven, Zhetar, Zhat | Harvesting month
row 8 : August | Август | Avgust | Орач, Зарев | Orach, Zarev | Tillage month
row 9 : September | Септември | Septemvri | Руен, Руй | Ruen, Ruy | Bounteous month
row 10 : October | Октомври | Oktomvri | Листопад | Listopad | Month of falling leaves
row 11 : November | Ноември | Noemvri | Груден | Gruden | Month of tubers
row 12 : December | Декември | Dekemvri | Студен | Studen | Cold month [/Table] Answer this question based on the above table: What's the Bulgarian word for september?

Answer: Септември
[Table] header: English name | Bulgarian name | Bulgarian name ( Transliteration ) | Old Bulgarian Names | Old Bulgarian name (Transliteration) | Old Bulgarian name - Meaning row 1 : January | Януари | Yanuari | Просинец, Кол | Prosinets, Kolog, Kol | Month of burning trunks
row 2 : February | Февруари | Fevruari | Съчен, Сечко, | Sachen, Sechko, Malak se | Month of dreadful ice
row 3 : March | Март | Mart | Сух | Suh | Dry month
row 4 : April | Април | April | Брязок | Bryazok | Colorful month
row 5 : May | Май | May | Тръвен | Traven | Grass month
row 6 : June | Юни | Yuni | Изок, Червени | Izok, Chervenik | Red month
row 7 : July | Юли | Yuli | Чръвенъ, ж | Chraven, Zhetar, Zhat | Harvesting month
row 8 : August | Август | Avgust | Орач, Зарев | Orach, Zarev | Tillage month
row 9 : September | Септември | Septemvri | Руен, Руй | Ruen, Ruy | Bounteous month
row 10 : October | Октомври | Oktomvri | Листопад | Listopad | Month of falling leaves
row 11 : November | Ноември | Noemvri | Груден | Gruden | Month of tubers
row 12 : December | Декември | Dekemvri | Студен | Studen | Cold month [/Table] Answer this question based on the above table: What's the old Bulgarian word for may?

Answer: Grass month
[Table] header: Date | Opponent | Score | Loss | Save row 1 : May 1 | Giants | 4 - 3 | Garrelts (0-1) | || 14,490 ||13-6|
row 2 : May 3 | Padres | 6 - 5 | Sutcliffe (3-3) | Gossage (7)
row 3 : May 4 | Padres | 12 - 8 | Booker (0-1) | Smith (6)
row 4 : May 5 | Padres | 6 - 3 | DeLeón (0-2) | || 36,388 ||15-7
row 5 : May 6 | Dodgers | 5 - 4 10 | Fontenot (0-1) | || 20,796 ||15-8|
row 6 : May 7 | Dodgers | 4 - 2 | Brennan (1-2) | Smith (7)
row 7 : May 8 | @ Giants | 1 - 0 | Krukow (2-2) | || 16,134 ||17-8||
row 8 : May 9 | @ Giants | 1 - 0 12 | Brusstar (0-1) | || 11,825 ||17-9|
row 9 : May 10 | @ Padres | 6 - 2 | Hoyt (2-3) | Smith (8)
row 10 : May 11 | @ Padres | 3 - 1 | Eckersley (4-2) | Gossage (8)
row 11 : May 12 | @ Padres | 5 - 3 | Ruthven (1-2) | Gossage (9)
row 12 : May 14 | @ Dodgers | 8 - 3 | Valenzuela (3-4) | || 48,187 ||19-11|
row 13 : May 15 | @ Dodgers | 3 - 2 | Honeycutt (2-3) | Smith (9)
row 14 : May 17 | @ Braves | 7 - 5 | Sutter (2-1) | || 20,901 ||21-11|
row 15 : May 18 | @ Braves | 4 - 3 | Eckersley (4-3) | Sutter (7)
row 16 : May 19 | @ Braves | 3 - 0 | Sutcliffe (5-4) | Sutter (8)
row 17 : May 20 | Reds | 6 - 1 | Tibbs (3-6) | || 21,231 ||22-13|
row 18 : May 21 | Reds | 5 - 2 | Frazier (2-1) | Power (8)
row 19 : May 22 | Reds | 7 - 4 | Browning (4-3) | Smith (10)
row 20 : May 24 | Astros | 6 - 2 | Ruthven (1-3) | || 35,743 ||23-15
row 21 : May 25 | Astros | 5 - 4 | Ross (0-1) | || 36,278 ||24-15
row 22 : May 26 | Astros | 10 - 8 | Ross (0-2) | Smith (11)
row 23 : May 27 | @ Reds | 4 - 3 | Browning (4-4) | Smith (12)
row 24 : May 28 | @ Reds | 13 - 11 | Sorensen (1-1) | || 15,121 ||26-16||
row 25 : May 29 | @ Reds | 1 - 0 | Ruthven (1-4) | || 18,402 ||26-17|
row 26 : May 31 | @ Astros | 6 - 2 10 | Smith (4-2) | || 33,440 ||27-17| [/Table] Answer this question based on the above table: Who was the Opponent when the Save was || 36,388 ||15-7||?

Answer: none
[Table] header: Date | Opponent | Score | Loss | Save row 1 : May 1 | Giants | 4 - 3 | Garrelts (0-1) | || 14,490 ||13-6|
row 2 : May 3 | Padres | 6 - 5 | Sutcliffe (3-3) | Gossage (7)
row 3 : May 4 | Padres | 12 - 8 | Booker (0-1) | Smith (6)
row 4 : May 5 | Padres | 6 - 3 | DeLeón (0-2) | || 36,388 ||15-7
row 5 : May 6 | Dodgers | 5 - 4 10 | Fontenot (0-1) | || 20,796 ||15-8|
row 6 : May 7 | Dodgers | 4 - 2 | Brennan (1-2) | Smith (7)
row 7 : May 8 | @ Giants | 1 - 0 | Krukow (2-2) | || 16,134 ||17-8||
row 8 : May 9 | @ Giants | 1 - 0 12 | Brusstar (0-1) | || 11,825 ||17-9|
row 9 : May 10 | @ Padres | 6 - 2 | Hoyt (2-3) | Smith (8)
row 10 : May 11 | @ Padres | 3 - 1 | Eckersley (4-2) | Gossage (8)
row 11 : May 12 | @ Padres | 5 - 3 | Ruthven (1-2) | Gossage (9)
row 12 : May 14 | @ Dodgers | 8 - 3 | Valenzuela (3-4) | || 48,187 ||19-11|
row 13 : May 15 | @ Dodgers | 3 - 2 | Honeycutt (2-3) | Smith (9)
row 14 : May 17 | @ Braves | 7 - 5 | Sutter (2-1) | || 20,901 ||21-11|
row 15 : May 18 | @ Braves | 4 - 3 | Eckersley (4-3) | Sutter (7)
row 16 : May 19 | @ Braves | 3 - 0 | Sutcliffe (5-4) | Sutter (8)
row 17 : May 20 | Reds | 6 - 1 | Tibbs (3-6) | || 21,231 ||22-13|
row 18 : May 21 | Reds | 5 - 2 | Frazier (2-1) | Power (8)
row 19 : May 22 | Reds | 7 - 4 | Browning (4-3) | Smith (10)
row 20 : May 24 | Astros | 6 - 2 | Ruthven (1-3) | || 35,743 ||23-15
row 21 : May 25 | Astros | 5 - 4 | Ross (0-1) | || 36,278 ||24-15
row 22 : May 26 | Astros | 10 - 8 | Ross (0-2) | Smith (11)
row 23 : May 27 | @ Reds | 4 - 3 | Browning (4-4) | Smith (12)
row 24 : May 28 | @ Reds | 13 - 11 | Sorensen (1-1) | || 15,121 ||26-16||
row 25 : May 29 | @ Reds | 1 - 0 | Ruthven (1-4) | || 18,402 ||26-17|
row 26 : May 31 | @ Astros | 6 - 2 10 | Smith (4-2) | || 33,440 ||27-17| [/Table] Answer this question based on the above table: What Opponent played on the Date May 21?

Answer: Reds
[Table] header: Date | Opponent | Score | Loss | Save row 1 : May 1 | Giants | 4 - 3 | Garrelts (0-1) | || 14,490 ||13-6|
row 2 : May 3 | Padres | 6 - 5 | Sutcliffe (3-3) | Gossage (7)
row 3 : May 4 | Padres | 12 - 8 | Booker (0-1) | Smith (6)
row 4 : May 5 | Padres | 6 - 3 | DeLeón (0-2) | || 36,388 ||15-7
row 5 : May 6 | Dodgers | 5 - 4 10 | Fontenot (0-1) | || 20,796 ||15-8|
row 6 : May 7 | Dodgers | 4 - 2 | Brennan (1-2) | Smith (7)
row 7 : May 8 | @ Giants | 1 - 0 | Krukow (2-2) | || 16,134 ||17-8||
row 8 : May 9 | @ Giants | 1 - 0 12 | Brusstar (0-1) | || 11,825 ||17-9|
row 9 : May 10 | @ Padres | 6 - 2 | Hoyt (2-3) | Smith (8)
row 10 : May 11 | @ Padres | 3 - 1 | Eckersley (4-2) | Gossage (8)
row 11 : May 12 | @ Padres | 5 - 3 | Ruthven (1-2) | Gossage (9)
row 12 : May 14 | @ Dodgers | 8 - 3 | Valenzuela (3-4) | || 48,187 ||19-11|
row 13 : May 15 | @ Dodgers | 3 - 2 | Honeycutt (2-3) | Smith (9)
row 14 : May 17 | @ Braves | 7 - 5 | Sutter (2-1) | || 20,901 ||21-11|
row 15 : May 18 | @ Braves | 4 - 3 | Eckersley (4-3) | Sutter (7)
row 16 : May 19 | @ Braves | 3 - 0 | Sutcliffe (5-4) | Sutter (8)
row 17 : May 20 | Reds | 6 - 1 | Tibbs (3-6) | || 21,231 ||22-13|
row 18 : May 21 | Reds | 5 - 2 | Frazier (2-1) | Power (8)
row 19 : May 22 | Reds | 7 - 4 | Browning (4-3) | Smith (10)
row 20 : May 24 | Astros | 6 - 2 | Ruthven (1-3) | || 35,743 ||23-15
row 21 : May 25 | Astros | 5 - 4 | Ross (0-1) | || 36,278 ||24-15
row 22 : May 26 | Astros | 10 - 8 | Ross (0-2) | Smith (11)
row 23 : May 27 | @ Reds | 4 - 3 | Browning (4-4) | Smith (12)
row 24 : May 28 | @ Reds | 13 - 11 | Sorensen (1-1) | || 15,121 ||26-16||
row 25 : May 29 | @ Reds | 1 - 0 | Ruthven (1-4) | || 18,402 ||26-17|
row 26 : May 31 | @ Astros | 6 - 2 10 | Smith (4-2) | || 33,440 ||27-17| [/Table] Answer this question based on the above table: What was the Score when the Loss was ruthven (1-4)?

Answer: 1 - 0
[Table] header: Pick # | NFL Team | Player | Position | College row 1 : 12 | Green Bay Packers NFL | Rich Moore | Defensive Tackle | Villanova
row 2 : 38 | Green Bay Packers NFL | Dave Bradley | Tackle | Penn State
row 3 : 64 | Green Bay Packers NFL | John Spilis | Wide Receiver | Northern Illinois
row 4 : 90 | Green Bay Packers NFL | Perry Williams | Running Back | Purdue
row 5 : 116 | Green Bay Packers NFL | Bill Hayhoe | Defensive Tackle | USC
row 6 : 142 | Green Bay Packers NFL | Ken Vinyard | Placekicker | Texas Tech
row 7 : 168 | Green Bay Packers NFL | Larry Agajanian | Defensive Tackle | UCLA
row 8 : 194 | Green Bay Packers NFL | Doug Gosnell | Defensive Tackle | Utah State
row 9 : 220 | Green Bay Packers NFL | Dave Hampton | Running Back | Wyoming
row 10 : 246 | Green Bay Packers NFL | Bruce Nelson | Tackle | North Dakota State
row 11 : 272 | Green Bay Packers NFL | Leon Harden | Defensive Back | UTEP
row 12 : 298 | Green Bay Packers NFL | Tom Buckman | Tight End | Texas A&M
row 13 : 324 | Green Bay Packers NFL | Craig Koinzan | Linebacker | Doane
row 14 : 350 | Green Bay Packers NFL | Rich Voltzke | Running Back | Minnesota-Duluth
row 15 : 376 | Green Bay Packers NFL | Dan Eckstein | Defensive Back | Presbyterian
row 16 : 402 | Green Bay Packers NFL | Dick Hewins | Wide Receiver | Drake
row 17 : 428 | Green Bay Packers NFL | John Mack | Running Back | Central Missouri State [/Table] Answer this question based on the above table: What was the highest pick for Penn State?

Answer: 38.0
[Table] header: Pick # | NFL Team | Player | Position | College row 1 : 12 | Green Bay Packers NFL | Rich Moore | Defensive Tackle | Villanova
row 2 : 38 | Green Bay Packers NFL | Dave Bradley | Tackle | Penn State
row 3 : 64 | Green Bay Packers NFL | John Spilis | Wide Receiver | Northern Illinois
row 4 : 90 | Green Bay Packers NFL | Perry Williams | Running Back | Purdue
row 5 : 116 | Green Bay Packers NFL | Bill Hayhoe | Defensive Tackle | USC
row 6 : 142 | Green Bay Packers NFL | Ken Vinyard | Placekicker | Texas Tech
row 7 : 168 | Green Bay Packers NFL | Larry Agajanian | Defensive Tackle | UCLA
row 8 : 194 | Green Bay Packers NFL | Doug Gosnell | Defensive Tackle | Utah State
row 9 : 220 | Green Bay Packers NFL | Dave Hampton | Running Back | Wyoming
row 10 : 246 | Green Bay Packers NFL | Bruce Nelson | Tackle | North Dakota State
row 11 : 272 | Green Bay Packers NFL | Leon Harden | Defensive Back | UTEP
row 12 : 298 | Green Bay Packers NFL | Tom Buckman | Tight End | Texas A&M
row 13 : 324 | Green Bay Packers NFL | Craig Koinzan | Linebacker | Doane
row 14 : 350 | Green Bay Packers NFL | Rich Voltzke | Running Back | Minnesota-Duluth
row 15 : 376 | Green Bay Packers NFL | Dan Eckstein | Defensive Back | Presbyterian
row 16 : 402 | Green Bay Packers NFL | Dick Hewins | Wide Receiver | Drake
row 17 : 428 | Green Bay Packers NFL | John Mack | Running Back | Central Missouri State [/Table] Answer this question based on the above table: What position does pick number 12 play?

Answer: Defensive Tackle
[Table] header: Pick # | NFL Team | Player | Position | College row 1 : 12 | Green Bay Packers NFL | Rich Moore | Defensive Tackle | Villanova
row 2 : 38 | Green Bay Packers NFL | Dave Bradley | Tackle | Penn State
row 3 : 64 | Green Bay Packers NFL | John Spilis | Wide Receiver | Northern Illinois
row 4 : 90 | Green Bay Packers NFL | Perry Williams | Running Back | Purdue
row 5 : 116 | Green Bay Packers NFL | Bill Hayhoe | Defensive Tackle | USC
row 6 : 142 | Green Bay Packers NFL | Ken Vinyard | Placekicker | Texas Tech
row 7 : 168 | Green Bay Packers NFL | Larry Agajanian | Defensive Tackle | UCLA
row 8 : 194 | Green Bay Packers NFL | Doug Gosnell | Defensive Tackle | Utah State
row 9 : 220 | Green Bay Packers NFL | Dave Hampton | Running Back | Wyoming
row 10 : 246 | Green Bay Packers NFL | Bruce Nelson | Tackle | North Dakota State
row 11 : 272 | Green Bay Packers NFL | Leon Harden | Defensive Back | UTEP
row 12 : 298 | Green Bay Packers NFL | Tom Buckman | Tight End | Texas A&M
row 13 : 324 | Green Bay Packers NFL | Craig Koinzan | Linebacker | Doane
row 14 : 350 | Green Bay Packers NFL | Rich Voltzke | Running Back | Minnesota-Duluth
row 15 : 376 | Green Bay Packers NFL | Dan Eckstein | Defensive Back | Presbyterian
row 16 : 402 | Green Bay Packers NFL | Dick Hewins | Wide Receiver | Drake
row 17 : 428 | Green Bay Packers NFL | John Mack | Running Back | Central Missouri State [/Table] Answer this question based on the above table: What is the pick number for the College of Central Missouri State?

Answer: 428.0
[Table] header: Pick # | NFL Team | Player | Position | College row 1 : 12 | Green Bay Packers NFL | Rich Moore | Defensive Tackle | Villanova
row 2 : 38 | Green Bay Packers NFL | Dave Bradley | Tackle | Penn State
row 3 : 64 | Green Bay Packers NFL | John Spilis | Wide Receiver | Northern Illinois
row 4 : 90 | Green Bay Packers NFL | Perry Williams | Running Back | Purdue
row 5 : 116 | Green Bay Packers NFL | Bill Hayhoe | Defensive Tackle | USC
row 6 : 142 | Green Bay Packers NFL | Ken Vinyard | Placekicker | Texas Tech
row 7 : 168 | Green Bay Packers NFL | Larry Agajanian | Defensive Tackle | UCLA
row 8 : 194 | Green Bay Packers NFL | Doug Gosnell | Defensive Tackle | Utah State
row 9 : 220 | Green Bay Packers NFL | Dave Hampton | Running Back | Wyoming
row 10 : 246 | Green Bay Packers NFL | Bruce Nelson | Tackle | North Dakota State
row 11 : 272 | Green Bay Packers NFL | Leon Harden | Defensive Back | UTEP
row 12 : 298 | Green Bay Packers NFL | Tom Buckman | Tight End | Texas A&M
row 13 : 324 | Green Bay Packers NFL | Craig Koinzan | Linebacker | Doane
row 14 : 350 | Green Bay Packers NFL | Rich Voltzke | Running Back | Minnesota-Duluth
row 15 : 376 | Green Bay Packers NFL | Dan Eckstein | Defensive Back | Presbyterian
row 16 : 402 | Green Bay Packers NFL | Dick Hewins | Wide Receiver | Drake
row 17 : 428 | Green Bay Packers NFL | John Mack | Running Back | Central Missouri State [/Table] Answer this question based on the above table: What was Rich Voltzke's highest pick number?

Answer: 350.0
[Table] header: Games | Drawn | Lost | Points difference | Points row 1 : 7 | 0 | 0 | 48 - 21 | 14
row 2 : 7 | 1 | 1 | 31 - 21 | 11
row 3 : 7 | 2 | 2 | 26 - 22 | 8
row 4 : 7 | 3 | 2 | 22 - 22 | 7
row 5 : 7 | 2 | 3 | 19 - 24 | 6
row 6 : 7 | 1 | 4 | 28 - 22 | 5
row 7 : 7 | 1 | 4 | 21 - 38 | 5
row 8 : 7 | 0 | 7 | 19 - 44 | 0 [/Table] Answer this question based on the above table: How many Games has Points larger than 0 and a Lost smaller than 4?

Answer: 0.0
[Table] header: Games | Drawn | Lost | Points difference | Points row 1 : 7 | 0 | 0 | 48 - 21 | 14
row 2 : 7 | 1 | 1 | 31 - 21 | 11
row 3 : 7 | 2 | 2 | 26 - 22 | 8
row 4 : 7 | 3 | 2 | 22 - 22 | 7
row 5 : 7 | 2 | 3 | 19 - 24 | 6
row 6 : 7 | 1 | 4 | 28 - 22 | 5
row 7 : 7 | 1 | 4 | 21 - 38 | 5
row 8 : 7 | 0 | 7 | 19 - 44 | 0 [/Table] Answer this question based on the above table: which Points has Games smaller than 7?

Answer: none
[Table] header: Games | Drawn | Lost | Points difference | Points row 1 : 7 | 0 | 0 | 48 - 21 | 14
row 2 : 7 | 1 | 1 | 31 - 21 | 11
row 3 : 7 | 2 | 2 | 26 - 22 | 8
row 4 : 7 | 3 | 2 | 22 - 22 | 7
row 5 : 7 | 2 | 3 | 19 - 24 | 6
row 6 : 7 | 1 | 4 | 28 - 22 | 5
row 7 : 7 | 1 | 4 | 21 - 38 | 5
row 8 : 7 | 0 | 7 | 19 - 44 | 0 [/Table] Answer this question based on the above table: How many Drawns have Points smaller than 5 and a Lost larger than 7?

Answer: 0.0
[Table] header: Date | Visitor | Score | Home | Record row 1 : January 2 | St. Louis | 3–1 | Vancouver | 14–21–3
row 2 : January 6 | Vancouver | 3–7 | Montreal | 14–22–3
row 3 : January 7 | Vancouver | 4–6 | Boston | 14–23–3
row 4 : January 9 | Vancouver | 2–2 | St. Louis | 14–23–4
row 5 : January 12 | New York | 4–2 | Vancouver | 14–24–4
row 6 : January 16 | Pittsburgh | 4–3 | Vancouver | 14–25–4
row 7 : January 17 | Vancouver | 3–1 | California | 15–25–4
row 8 : January 20 | Toronto | 5–1 | Vancouver | 15–26–4
row 9 : January 23 | Vancouver | 1–4 | Pittsburgh | 15–27–4
row 10 : January 24 | Vancouver | 3–7 | Detroit | 15–28–4
row 11 : January 26 | Chicago | 3–3 | Vancouver | 15–28–5
row 12 : January 29 | Minnesota | 2–1 | Vancouver | 15–29–5
row 13 : January 31 | Buffalo | 6–1 | Vancouver | 15–30–5 [/Table] Answer this question based on the above table: Which record has a date of January 29?

Answer: 15–29–5
[Table] header: Date | Visitor | Score | Home | Record row 1 : January 2 | St. Louis | 3–1 | Vancouver | 14–21–3
row 2 : January 6 | Vancouver | 3–7 | Montreal | 14–22–3
row 3 : January 7 | Vancouver | 4–6 | Boston | 14–23–3
row 4 : January 9 | Vancouver | 2–2 | St. Louis | 14–23–4
row 5 : January 12 | New York | 4–2 | Vancouver | 14–24–4
row 6 : January 16 | Pittsburgh | 4–3 | Vancouver | 14–25–4
row 7 : January 17 | Vancouver | 3–1 | California | 15–25–4
row 8 : January 20 | Toronto | 5–1 | Vancouver | 15–26–4
row 9 : January 23 | Vancouver | 1–4 | Pittsburgh | 15–27–4
row 10 : January 24 | Vancouver | 3–7 | Detroit | 15–28–4
row 11 : January 26 | Chicago | 3–3 | Vancouver | 15–28–5
row 12 : January 29 | Minnesota | 2–1 | Vancouver | 15–29–5
row 13 : January 31 | Buffalo | 6–1 | Vancouver | 15–30–5 [/Table] Answer this question based on the above table: Which score has a visitor of New York?

Answer: 4–2
[Table] header: Date | Visitor | Score | Home | Record row 1 : January 2 | St. Louis | 3–1 | Vancouver | 14–21–3
row 2 : January 6 | Vancouver | 3–7 | Montreal | 14–22–3
row 3 : January 7 | Vancouver | 4–6 | Boston | 14–23–3
row 4 : January 9 | Vancouver | 2–2 | St. Louis | 14–23–4
row 5 : January 12 | New York | 4–2 | Vancouver | 14–24–4
row 6 : January 16 | Pittsburgh | 4–3 | Vancouver | 14–25–4
row 7 : January 17 | Vancouver | 3–1 | California | 15–25–4
row 8 : January 20 | Toronto | 5–1 | Vancouver | 15–26–4
row 9 : January 23 | Vancouver | 1–4 | Pittsburgh | 15–27–4
row 10 : January 24 | Vancouver | 3–7 | Detroit | 15–28–4
row 11 : January 26 | Chicago | 3–3 | Vancouver | 15–28–5
row 12 : January 29 | Minnesota | 2–1 | Vancouver | 15–29–5
row 13 : January 31 | Buffalo | 6–1 | Vancouver | 15–30–5 [/Table] Answer this question based on the above table: What is the name of the visitor on January 24?

Answer: Vancouver