niryuu/sni-each-converted huggingface dataset:
greater { hop { filter_eq { all_rows ; goalkeeper ; juan calatayud } ; goals } ; hop { filter_eq { all_rows ; goalkeeper ; eduardo navarro } ; goals } } = true
Output: select the rows whose goalkeeper record fuzzily matches to juan calatayud . take the goals record of this row . select the rows whose goalkeeper record fuzzily matches to eduardo navarro . take the goals record of this row . the first record is greater than the second record .
eq { diff { hop { filter_eq { all_rows ; incumbent ; doug walgren } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; tom ridge } ; first elected } } ; -6 } = true
Output: select the rows whose incumbent record fuzzily matches to doug walgren . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to tom ridge . take the first elected record of this row . the second record is 6 larger than the first record .
eq { hop { argmax { all_rows ; attendance } ; opponent } ; green bay packers } = true
Output: select the row whose attendance record of all rows is maximum . the opponent record of this row is green bay packers .
most_eq { all_rows ; details ; abandoned part of northern heights project } = true
Output: for the details records of all rows , most of them fuzzily match to abandoned part of northern heights project .
less { hop { filter_eq { all_rows ; sport ; m cross country } ; date } ; hop { filter_eq { all_rows ; sport ; w gymnastics } ; date } } = true
Output: select the rows whose sport record fuzzily matches to m cross country . take the date record of this row . select the rows whose sport record fuzzily matches to w gymnastics . take the date record of this row . the first record is less than the second record .
eq { count { filter_greater { all_rows ; earnings ; 4000000 } } ; 2 } = true
Output: select the rows whose earnings record is greater than 4000000 . the number of such rows is 2 .
round_eq { avg { all_rows ; away team score } ; 10.55 ( 74 ) } = true
Output: the average of the away team score record of all rows is 10.55  74  .
most_eq { all_rows ; manner of departure ; resigned } = true
Output: for the manner of departure records of all rows , most of them fuzzily match to resigned .
less { hop { filter_eq { all_rows ; opposing team ; queensland } ; against } ; hop { filter_eq { all_rows ; opposing team ; new south wales } ; against } } = true
Output: select the rows whose opposing team record fuzzily matches to queensland . take the against record of this row . select the rows whose opposing team record fuzzily matches to new south wales . take the against record of this row . the first record is less than the second record .
eq { count { filter_greater_eq { filter_eq { all_rows ; home ; minnesota } ; attendance ; 19360 } } ; 2 } = true
Output: select the rows whose home record fuzzily matches to minnesota . among these rows , select the rows whose attendance record is greater than or equal to 19360 . the number of such rows is 2 .
eq { max { filter_eq { all_rows ; winner ; rangers } ; season } ; 1944 - 45 } = true
Output: select the rows whose winner record fuzzily matches to rangers . the maximum season record of these rows is 1944  45 .
all_greater_eq { filter_greater_eq { all_rows ; gold ; 1 } ; bronze ; 1 } = true
Output: select the rows whose gold record is greater than or equal to 1 . for the bronze records of these rows , all of them are greater than or equal to 1 .
eq { count { filter_eq { all_rows ; result ; re-elected } } ; 5 } = true
Output: select the rows whose result record fuzzily matches to reelected . the number of such rows is 5 .
and { only { filter_eq { all_rows ; home ; wizards } } ; eq { hop { filter_eq { all_rows ; home ; wizards } ; date } ; 4 february 2003 } } = true
Output: select the rows whose home record fuzzily matches to wizards . there is only one such row in the table . the date record of this unqiue row is 4 february 2003 .
greater { hop { filter_eq { all_rows ; date ; april 25 } ; high points } ; hop { filter_eq { all_rows ; date ; april 27 } ; high points } } = true
Output: select the rows whose date record fuzzily matches to april 25 . take the high points record of this row . select the rows whose date record fuzzily matches to april 27 . take the high points record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; td 's ; 2 } ; player } ; lenzie jackson } = true
Output: select the row whose td s record of all rows is 2nd maximum . the player record of this row is lenzie jackson .
eq { count { filter_eq { filter_eq { all_rows ; opponent ; gothenburg } ; date ; may } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to gothenburg . among these rows , select the rows whose date record fuzzily matches to may . the number of such rows is 2 .
eq { hop { argmax { all_rows ; points } ; team } ; paulistano } = true
Output: select the row whose points record of all rows is maximum . the team record of this row is paulistano .
eq { count { filter_eq { filter_eq { all_rows ; played ; 20 } ; drawn ; 1 } } ; 5 } = true
Output: select the rows whose played record is equal to 20 . among these rows , select the rows whose drawn record is equal to 1 . the number of such rows is 5 .
eq { hop { argmax { all_rows ; points } ; entrant } ; force india f1 team } = true
Output: select the row whose points record of all rows is maximum . the entrant record of this row is force india f1 team .
eq { count { filter_greater { filter_greater { all_rows ; number of electorates ( 2009 ) ; 150000 } ; number of electorates ( 2009 ) ; 150000 } } ; 2 } = true
Output: select the rows whose number of electorates  2009  record is greater than 150000 . among these rows , select the rows whose number of electorates  2009  record is greater than 150000 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; creative excellency ; atom egoyan } } ; eq { hop { filter_eq { all_rows ; creative excellency ; atom egoyan } ; year } ; 2006 } } = true
Output: select the rows whose creative excellency record fuzzily matches to atom egoyan . there is only one such row in the table . the year record of this unqiue row is 2006 .
eq { count { filter_eq { all_rows ; pick ; 19 } } ; 3 } = true
Output: select the rows whose pick record is equal to 19 . the number of such rows is 3 .
most_less { all_rows ; score ; 5 } = true
Output: for the score records of all rows , most of them are less than 5 .
most_eq { filter_eq { all_rows ; date ; november } ; result ; w } = true
Output: select the rows whose date record fuzzily matches to november . for the result records of these rows , most of them fuzzily match to w .
and { only { filter_eq { all_rows ; opponent ; oakland raiders } } ; eq { hop { filter_eq { all_rows ; opponent ; oakland raiders } ; date } ; october 6 , 1968 } } = true
Output: select the rows whose opponent record fuzzily matches to oakland raiders . there is only one such row in the table . the date record of this unqiue row is october 6 , 1968 .
eq { hop { argmax { all_rows ; assists } ; name } ; yotam halperin } = true
Output: select the row whose assists record of all rows is maximum . the name record of this row is yotam halperin .
round_eq { avg { all_rows ; against } ; 21 } = true
Output: the average of the against record of all rows is 21 .
eq { count { filter_eq { all_rows ; aspect ; 4:3 } } ; 7 } = true
Output: select the rows whose aspect record fuzzily matches to 4:3 . the number of such rows is 7 .
eq { hop { nth_argmin { all_rows ; week ; 1 } ; opponent } ; los angeles raiders } = true
Output: select the row whose week record of all rows is 1st minimum . the opponent record of this row is los angeles raiders .
all_greater { filter_less { all_rows ; position ; 2 } ; points ; 1000 } = true
Output: select the rows whose position record is less than 2 . for the points records of these rows , all of them are greater than 1000 .
eq { count { filter_eq { all_rows ; color ; green and light colors } } ; 2 } = true
Output: select the rows whose color record fuzzily matches to green and light colors . the number of such rows is 2 .
only { filter_eq { all_rows ; country ; england } } = true
Output: select the rows whose country record fuzzily matches to england . there is only one such row in the table .
and { only { filter_eq { all_rows ; champions ; sv sandhausen } } ; eq { hop { filter_eq { all_rows ; champions ; sv sandhausen } ; season } ; 2011 - 12 } } = true
Output: select the rows whose champions record fuzzily matches to sv sandhausen . there is only one such row in the table . the season record of this unqiue row is 2011  12 .
eq { count { filter_eq { all_rows ; graduated ; 2009 } } ; 5 } = true
Output: select the rows whose graduated record is equal to 2009 . the number of such rows is 5 .
and { only { filter_eq { all_rows ; sport ; basketball } } ; eq { hop { filter_eq { all_rows ; sport ; basketball } ; club } ; spartak st petersburg } } = true
Output: select the rows whose sport record fuzzily matches to basketball . there is only one such row in the table . the club record of this unqiue row is spartak st petersburg .
greater { hop { filter_eq { all_rows ; country ; dominican republic } ; asians } ; hop { filter_eq { all_rows ; country ; costa rica } ; asians } } = true
Output: select the rows whose country record fuzzily matches to dominican republic . take the asians record of this row . select the rows whose country record fuzzily matches to costa rica . take the asians record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; crowd } ; 18978 } = true
Output: the average of the crowd record of all rows is 18978 .
eq { count { filter_eq { all_rows ; owner ; harvard broadcasting } } ; 2 } = true
Output: select the rows whose owner record fuzzily matches to harvard broadcasting . the number of such rows is 2 .
and { only { filter_eq { all_rows ; opponent ; darren cahill } } ; eq { hop { filter_eq { all_rows ; opponent ; darren cahill } ; championship } ; gstaad , switzerland } } = true
Output: select the rows whose opponent record fuzzily matches to darren cahill . there is only one such row in the table . the championship record of this unqiue row is gstaad , switzerland .
eq { hop { nth_argmax { all_rows ; us viewers ( million ) ; 3 } ; title } ; blood and treasure } = true
Output: select the row whose us viewers  million  record of all rows is 3rd maximum . the title record of this row is blood and treasure .
eq { hop { argmax { all_rows ; original airdate } ; title } ; silent night } = true
Output: select the row whose original airdate record of all rows is maximum . the title record of this row is silent night .
eq { count { filter_eq { all_rows ; format ; lp } } ; 2 } = true
Output: select the rows whose format record fuzzily matches to lp . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; country ; united states } } ; 11 } = true
Output: select the rows whose country record fuzzily matches to united states . the number of such rows is 11 .
eq { hop { argmax { filter_eq { all_rows ; type ; village } ; population ( 2011 ) } ; settlement } ; kać } = true
Output: select the rows whose type record fuzzily matches to village . select the row whose population  2011  record of these rows is maximum . the settlement record of this row is kać .
eq { count { filter_eq { all_rows ; reason for change ; resigned december 30 , 1966 } } ; 9 } = true
Output: select the rows whose reason for change record fuzzily matches to resigned december 30 , 1966 . the number of such rows is 9 .
and { only { filter_eq { all_rows ; location ; marquette , michigan } } ; eq { hop { filter_eq { all_rows ; location ; marquette , michigan } ; institution } ; northern michigan university } } = true
Output: select the rows whose location record fuzzily matches to marquette , michigan . there is only one such row in the table . the institution record of this unqiue row is northern michigan university .
eq { hop { nth_argmax { all_rows ; june 14 , 2009 ; 2 } ; mexico } ; croatia } = true
Output: select the row whose june 14 , 2009 record of all rows is 2nd maximum . the mexico record of this row is croatia .
eq { hop { argmax { all_rows ; grand total } ; nato member } ; uk } = true
Output: select the row whose grand total record of all rows is maximum . the nato member record of this row is uk .
most_eq { all_rows ; date ; 26 january 1985 } = true
Output: for the date records of all rows , most of them fuzzily match to 26 january 1985 .
eq { hop { argmax { all_rows ; home team score } ; home team } ; st kilda } = true
Output: select the row whose home team score record of all rows is maximum . the home team record of this row is st kilda .
eq { count { filter_eq { all_rows ; region ; imereti } } ; 8 } = true
Output: select the rows whose region record fuzzily matches to imereti . the number of such rows is 8 .
round_eq { sum { filter_less_eq { all_rows ; rank ; 3 } ; total } ; 85 } = true
Output: select the rows whose rank record is less than or equal to 3 . the sum of the total record of these rows is 85 .
eq { hop { argmin { all_rows ; place } ; player } ; davis love iii } = true
Output: select the row whose place record of all rows is minimum . the player record of this row is davis love iii .
eq { count { filter_eq { all_rows ; opponent ; 64.2 % } } ; 1 } = true
Output: select the rows whose opponent record fuzzily matches to 64.2  . the number of such rows is 1 .
eq { count { filter_eq { all_rows ; us viewers ( in millions ) ; 3.69 } } ; 2 } = true
Output: select the rows whose us viewers  in millions  record is equal to 3.69 . the number of such rows is 2 .
all_eq { all_rows ; away captain ; prosper utseya } = true
Output: for the away captain records of all rows , all of them fuzzily match to prosper utseya .
round_eq { avg { all_rows ; weight } ; 95.5 } = true
Output: the average of the weight record of all rows is 95.5 .
round_eq { sum { filter_eq { all_rows ; class ; b } ; no built } ; 58 } = true
Output: select the rows whose class record fuzzily matches to b . the sum of the no built record of these rows is 58 .
eq { count { filter_eq { filter_eq { all_rows ; winner ; bobby clarke } ; goals ; 51 } } ; 2 } = true
Output: select the rows whose winner record fuzzily matches to bobby clarke . among these rows , select the rows whose goals record is equal to 51 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; tournament ; us open } ; events } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; events } } = true
Output: select the rows whose tournament record fuzzily matches to us open . take the events record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the events record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; vessels ; 2 } ; ship name } ; theofilos } = true
Output: select the row whose vessels record of all rows is 2nd maximum . the ship name record of this row is theofilos .
eq { hop { argmax { all_rows ; weight ( lb ) } ; name } ; gary mcghee } = true
Output: select the row whose weight  lb  record of all rows is maximum . the name record of this row is gary mcghee .
round_eq { avg { all_rows ; days with frost } ; 18.4 } = true
Output: the average of the days with frost record of all rows is 18.4 .
most_eq { filter_eq { all_rows ; location attendance ; skydome } ; high points ; damon stoudamire } = true
Output: select the rows whose location attendance record fuzzily matches to skydome . for the high points records of these rows , most of them fuzzily match to damon stoudamire .
and { only { filter_eq { all_rows ; result ; lost } } ; eq { hop { filter_eq { all_rows ; result ; lost } ; district } ; illinois 3 } } = true
Output: select the rows whose result record fuzzily matches to lost . there is only one such row in the table . the district record of this unqiue row is illinois 3 .
eq { hop { nth_argmin { filter_eq { all_rows ; constructor ; brm } ; time / retired ; 3 } ; driver } ; lucien bianchi } = true
Output: select the rows whose constructor record fuzzily matches to brm . select the row whose time  retired record of these rows is 3rd minimum . the driver record of this row is lucien bianchi .
eq { hop { argmax { filter_eq { all_rows ; team ; minardi team usa } ; laps } ; driver } ; robert doornbos } = true
Output: select the rows whose team record fuzzily matches to minardi team usa . select the row whose laps record of these rows is maximum . the driver record of this row is robert doornbos .
and { only { filter_eq { all_rows ; crystal structure ; orthorhombic } } ; eq { hop { filter_eq { all_rows ; crystal structure ; orthorhombic } ; formula } ; yba 2 cu 3 o 7 } } = true
Output: select the rows whose crystal structure record fuzzily matches to orthorhombic . there is only one such row in the table . the formula record of this unqiue row is yba 2 cu 3 o 7 .
and { only { filter_eq { all_rows ; laps ; 1 } } ; and { eq { hop { filter_eq { all_rows ; laps ; 1 } ; year } ; 1964 } ; eq { hop { filter_eq { all_rows ; laps ; 1 } ; finish } ; 30 } } } = true
Output: select the rows whose laps record is equal to 1 . there is only one such row in the table . the year record of this unqiue row is 1964 . the finish record of this unqiue row is 30 .
round_eq { avg { all_rows ; attendance } ; 61409 } = true
Output: the average of the attendance record of all rows is 61409 .
most_greater { all_rows ; high points ; 20 } = true
Output: for the high points records of all rows , most of them are greater than 20 .
round_eq { avg { all_rows ; matches } ; 8.71 } = true
Output: the average of the matches record of all rows is 8.71 .
round_eq { avg { all_rows ; top - 5 } ; .5 } = true
Output: the average of the top  5 record of all rows is .5 .
and { only { filter_eq { filter_less { all_rows ; 50 m split ; 29.0 } ; nationality ; zimbabwe } } ; eq { hop { filter_eq { filter_less { all_rows ; 50 m split ; 29.0 } ; nationality ; zimbabwe } ; name } ; kirsty coventry } } = true
Output: select the rows whose 50 m split record is less than 29.0 . among these rows , select the rows whose nationality record fuzzily matches to zimbabwe . there is only one such row in the table . the name record of this unqiue row is kirsty coventry .
only { filter_greater { filter_less_eq { all_rows ; position ; 5 } ; drawn ; 3 } } = true
Output: select the rows whose position record is less than or equal to 5 . among these rows , select the rows whose drawn record is greater than 3 . there is only one such row in the table .
and { only { filter_eq { all_rows ; nickname ; polar bears } } ; eq { hop { filter_eq { all_rows ; nickname ; polar bears } ; school } ; jackson } } = true
Output: select the rows whose nickname record fuzzily matches to polar bears . there is only one such row in the table . the school record of this unqiue row is jackson .
and { eq { hop { filter_eq { all_rows ; name ; ishara amerasinghe } ; wickets } ; hop { filter_eq { all_rows ; name ; muttiah muralitharan } ; wickets } } ; and { eq { hop { filter_eq { all_rows ; name ; ishara amerasinghe } ; wickets } ; 9 } ; eq { hop { filter_eq { all_rows ; name ; muttiah muralitharan } ; wickets } ; 9 } } } = true
Output: select the rows whose name record fuzzily matches to ishara amerasinghe . take the wickets record of this row . select the rows whose name record fuzzily matches to muttiah muralitharan . take the wickets record of this row . the first record is equal to the second record . the wickets record of the first row is 9 . the wickets record of the second row is 9 .
less { hop { filter_eq { all_rows ; year ; 1991 } ; finish } ; hop { filter_eq { all_rows ; year ; 1993 } ; finish } } = true
Output: select the rows whose year record fuzzily matches to 1991 . take the finish record of this row . select the rows whose year record fuzzily matches to 1993 . take the finish record of this row . the first record is less than the second record .
most_greater { all_rows ; result ; 0 } = true
Output: for the result records of all rows , most of them are greater than 0 .
and { only { filter_eq { filter_eq { all_rows ; draws ; 4 } ; conceded ; 6 } } ; eq { hop { filter_eq { filter_eq { all_rows ; draws ; 4 } ; conceded ; 6 } ; team } ; olimpia } } = true
Output: select the rows whose draws record is equal to 4 . among these rows , select the rows whose conceded record is equal to 6 . there is only one such row in the table . the team record of this unqiue row is olimpia .
most_less { all_rows ; first elected ; 1920 } = true
Output: for the first elected records of all rows , most of them are less than 1920 .
eq { count { filter_eq { all_rows ; owner ; peter vegso } } ; 3 } = true
Output: select the rows whose owner record fuzzily matches to peter vegso . the number of such rows is 3 .
and { only { filter_eq { all_rows ; town ships ; 18 } } ; eq { hop { filter_eq { all_rows ; town ships ; 18 } ; state / region } ; kachin state } } = true
Output: select the rows whose town ships record is equal to 18 . there is only one such row in the table . the state  region record of this unqiue row is kachin state .
eq { count { filter_eq { all_rows ; country ; united states } } ; 4 } = true
Output: select the rows whose country record fuzzily matches to united states . the number of such rows is 4 .
eq { count { filter_less { filter_greater { all_rows ; population ; 50000 } ; altitude ( mslm ) ; 300 } } ; 2 } = true
Output: select the rows whose population record is greater than 50000 . among these rows , select the rows whose altitude  mslm  record is less than 300 . the number of such rows is 2 .
all_eq { all_rows ; l3 cache ; 3 mb } = true
Output: for the l3 cache records of all rows , all of them fuzzily match to 3 mb .
most_eq { all_rows ; location ; arlington } = true
Output: for the location records of all rows , most of them fuzzily match to arlington .
eq { count { filter_eq { all_rows ; date ; august } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to august . the number of such rows is 2 .
eq { diff { hop { filter_eq { all_rows ; name ; northern eagle } ; year } ; hop { filter_eq { all_rows ; name ; northern hawk } ; year } } ; -15 years } = true
Output: select the rows whose name record fuzzily matches to northern eagle . take the year record of this row . select the rows whose name record fuzzily matches to northern hawk . take the year record of this row . the second record is 15 years larger than the first record .
eq { count { filter_eq { filter_eq { all_rows ; location ; sheffield , england } ; type ; club trophy } } ; 2 } = true
Output: select the rows whose location record fuzzily matches to sheffield , england . among these rows , select the rows whose type record fuzzily matches to club trophy . the number of such rows is 2 .
most_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; completion schedule ; 2011 } = true
Output: select the rows whose state record fuzzily matches to jammu  kashmir . for the completion schedule records of these rows , most of them are equal to 2011 .
most_greater_eq { all_rows ; silver ; 1 } = true
Output: for the silver records of all rows , most of them are greater than or equal to 1 .
and { only { filter_eq { all_rows ; country ; japan } } ; eq { hop { filter_eq { all_rows ; country ; japan } ; rank } ; 4 } } = true
Output: select the rows whose country record fuzzily matches to japan . there is only one such row in the table . the rank record of this unqiue row is 4 .
eq { hop { nth_argmin { all_rows ; time ; 1 } ; name } ; leisel jones } = true
Output: select the row whose time record of all rows is 1st minimum . the name record of this row is leisel jones .
most_eq { all_rows ; final score ; l } = true
Output: for the final score records of all rows , most of them fuzzily match to l .
eq { hop { nth_argmax { all_rows ; 1910 census ; 3 } ; city } ; fuzan } = true
Output: select the row whose 1910 census record of all rows is 3rd maximum . the city record of this row is fuzan .
eq { hop { nth_argmax { filter_eq { all_rows ; event ; wimbledon } ; aces ; 1 } ; player } ; john isner } = true
Output: select the rows whose event record fuzzily matches to wimbledon . select the row whose aces record of these rows is 1st maximum . the player record of this row is john isner .
eq { nth_min { all_rows ; date ; 2 } ; 31 may } = true
Output: the 2nd minimum date record of all rows is 31 may .
eq { hop { nth_argmax { all_rows ; money ; 4 } ; score } ; 68 + 68 + 69 + 79 = 284 } = true
Output: select the row whose money record of all rows is 4th maximum . the score record of this row is 68 + 68 + 69 + 79 = 284 .
and { only { filter_eq { all_rows ; nationality ; greece } } ; eq { hop { filter_eq { all_rows ; nationality ; greece } ; name } ; anastasios gousis } } = true
Output: select the rows whose nationality record fuzzily matches to greece . there is only one such row in the table . the name record of this unqiue row is anastasios gousis .
round_eq { sum { all_rows ; crowd } ; 80962 } = true
Output: the sum of the crowd record of all rows is 80962 .
eq { count { filter_eq { filter_greater { all_rows ; attendance ; 50000 } ; date ; december } } ; 2 } = true
Output: select the rows whose attendance record is greater than 50000 . among these rows , select the rows whose date record fuzzily matches to december . the number of such rows is 2 .
and { greater { hop { filter_eq { all_rows ; away team ; richmond } ; away team score } ; hop { filter_eq { all_rows ; away team ; hawthorn } ; away team score } } ; and { eq { hop { filter_eq { all_rows ; away team ; richmond } ; away team score } ; 7.9 ( 51 ) } ; eq { hop { filter_eq { all_rows ; away team ; hawthorn } ; away team score } ; 7.7 ( 49 ) } } } = true
Output: select the rows whose away team record fuzzily matches to richmond . take the away team score record of this row . select the rows whose away team record fuzzily matches to hawthorn . take the away team score record of this row . the first record is greater than the second record . the away team score record of the first row is 7.9  51  . the away team score record of the second row is 7.7  49  .
eq { hop { argmin { all_rows ; position } ; year } ; 2009 } = true
Output: select the row whose position record of all rows is minimum . the year record of this row is 2009 .
and { only { filter_eq { all_rows ; result ; retired democratic hold } } ; eq { hop { filter_eq { all_rows ; result ; retired democratic hold } ; incumbent } ; william y humphreys } } = true
Output: select the rows whose result record fuzzily matches to retired democratic hold . there is only one such row in the table . the incumbent record of this unqiue row is william y humphreys .
eq { diff { hop { filter_eq { all_rows ; year ; 1997 } ; td 's } ; hop { filter_eq { all_rows ; year ; 1996 } ; td 's } } ; 2 } = true
Output: select the rows whose year record fuzzily matches to 1997 . take the td s record of this row . select the rows whose year record fuzzily matches to 1996 . take the td s record of this row . the first record is 2 larger than the second record .
and { greater { hop { filter_eq { all_rows ; home team ; collingwood } ; crowd } ; hop { filter_eq { all_rows ; home team ; carlton } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; home team ; collingwood } ; away team } ; south melbourne } ; eq { hop { filter_eq { all_rows ; home team ; carlton } ; away team } ; geelong } } } = true
Output: select the rows whose home team record fuzzily matches to collingwood . take the crowd record of this row . select the rows whose home team record fuzzily matches to carlton . take the crowd record of this row . the first record is greater than the second record . the away team record of the first row is south melbourne . the away team record of the second row is geelong .
only { filter_eq { filter_greater { all_rows ; date ; november 1 , 1955 } ; result ; l } } = true
Output: select the rows whose date record is greater than november 1 , 1955 . among these rows , select the rows whose result record fuzzily matches to l . there is only one such row in the table .
round_eq { avg { all_rows ; wk 11 } ; 15.4 } = true
Output: the average of the wk 11 record of all rows is 15.4 .
eq { count { filter_eq { all_rows ; successor ; vacant } } ; 2 } = true
Output: select the rows whose successor record fuzzily matches to vacant . the number of such rows is 2 .
most_eq { all_rows ; directed by ; ciaran donnelly } = true
Output: for the directed by records of all rows , most of them fuzzily match to ciaran donnelly .
eq { hop { argmax { all_rows ; matches } ; goalkeeper } ; chema } = true
Output: select the row whose matches record of all rows is maximum . the goalkeeper record of this row is chema .
most_eq { all_rows ; colors ; black } = true
Output: for the colors records of all rows , most of them fuzzily match to black .
eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; date } ; december 27 } = true
Output: select the row whose location attendance record of all rows is 1st maximum . the date record of this row is december 27 .
eq { hop { nth_argmin { filter_eq { all_rows ; champion ; laura davies } ; year ; 2 } ; margin of victory } ; 4 strokes } = true
Output: select the rows whose champion record fuzzily matches to laura davies . select the row whose year record of these rows is 2nd minimum . the margin of victory record of this row is 4 strokes .
eq { count { filter_eq { all_rows ; 2nd component ; acetone } } ; 3 } = true
Output: select the rows whose 2nd component record fuzzily matches to acetone . the number of such rows is 3 .
and { eq { hop { argmax { filter_eq { all_rows ; pos ; dnf } ; laps } ; year } ; 2005 } ; eq { hop { argmax { filter_eq { all_rows ; pos ; dnf } ; laps } ; class pos } ; dnf } } = true
Output: select the rows whose pos record fuzzily matches to dnf . select the row whose laps record of these rows is maximum . the year record of this row is 2005 . the class pos record of this row is dnf .
only { filter_eq { all_rows ; top - 5 ; 1 } } = true
Output: select the rows whose top  5 record is equal to 1 . there is only one such row in the table .
round_eq { avg { all_rows ; length ( ft ) } ; 86.25 } = true
Output: the average of the length  ft  record of all rows is 86.25 .
less { hop { filter_eq { all_rows ; channel ; rai 2 } ; launch date } ; hop { filter_eq { all_rows ; channel ; canale 5 } ; launch date } } = true
Output: select the rows whose channel record fuzzily matches to rai 2 . take the launch date record of this row . select the rows whose channel record fuzzily matches to canale 5 . take the launch date record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; pick } ; 94 } = true
Output: the average of the pick record of all rows is 94 .
eq { count { filter_eq { all_rows ; county ; kilkenny } } ; 4 } = true
Output: select the rows whose county record fuzzily matches to kilkenny . the number of such rows is 4 .
eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; wright patman } = true
Output: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is wright patman .
only { filter_greater { filter_eq { all_rows ; home team ; tennessee } ; attendance ; 100000 } } = true
Output: select the rows whose home team record fuzzily matches to tennessee . among these rows , select the rows whose attendance record is greater than 100000 . there is only one such row in the table .
eq { hop { nth_argmax { all_rows ; power ( kw ) ; 2 } ; branding } ; kingdom radio } = true
Output: select the row whose power  kw  record of all rows is 2nd maximum . the branding record of this row is kingdom radio .
and { eq { min { filter_eq { all_rows ; year ; 2008 } ; position } ; 1st } ; eq { hop { argmin { filter_eq { all_rows ; year ; 2008 } ; position } ; competition } ; central american and caribbean championships } } = true
Output: select the rows whose year record is equal to 2008 . the minimum position record of these rows is 1st . the competition record of the row with superlative position record is central american and caribbean championships .
and { only { filter_eq { all_rows ; discipline ; drag racing } } ; eq { hop { filter_eq { all_rows ; discipline ; drag racing } ; circuit } ; indianapolis raceway park } } = true
Output: select the rows whose discipline record fuzzily matches to drag racing . there is only one such row in the table . the circuit record of this unqiue row is indianapolis raceway park .
and { only { filter_eq { all_rows ; country ; spain } } ; eq { hop { filter_eq { all_rows ; country ; spain } ; player } ; josé maría olazábal } } = true
Output: select the rows whose country record fuzzily matches to spain . there is only one such row in the table . the player record of this unqiue row is josé maría olazábal .
eq { count { filter_eq { all_rows ; drawn ; 0 } } ; 4 } = true
Output: select the rows whose drawn record is equal to 0 . the number of such rows is 4 .
round_eq { sum { filter_eq { all_rows ; date ; friday , 16 february } ; crowd } ; 46,000 } = true
Output: select the rows whose date record fuzzily matches to friday , 16 february . the sum of the crowd record of these rows is 46,000 .
less { hop { filter_eq { all_rows ; school ; triton } ; year joined } ; hop { filter_eq { all_rows ; school ; oak hill } ; year joined } } = true
Output: select the rows whose school record fuzzily matches to triton . take the year joined record of this row . select the rows whose school record fuzzily matches to oak hill . take the year joined record of this row . the first record is less than the second record .
only { filter_eq { all_rows ; make ; dodge } } = true
Output: select the rows whose make record fuzzily matches to dodge . there is only one such row in the table .
eq { count { filter_eq { all_rows ; women 's singles ; gabriela zabavníková } } ; 3 } = true
Output: select the rows whose women s singles record fuzzily matches to gabriela zabavníková . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; agg ; 0 } } ; 1 } = true
Output: select the rows whose agg record fuzzily matches to 0 . the number of such rows is 1 .
most_eq { all_rows ; team name ; kemba racing } = true
Output: for the team name records of all rows , most of them fuzzily match to kemba racing .
eq { count { filter_eq { all_rows ; district ; chhatarpur } } ; 4 } = true
Output: select the rows whose district record fuzzily matches to chhatarpur . the number of such rows is 4 .
and { only { filter_eq { all_rows ; shirt sponsor ; mardan } } ; eq { hop { filter_eq { all_rows ; shirt sponsor ; mardan } ; team } ; antalyaspor } } = true
Output: select the rows whose shirt sponsor record fuzzily matches to mardan . there is only one such row in the table . the team record of this unqiue row is antalyaspor .
eq { count { filter_eq { filter_eq { all_rows ; directed by ; jonathan frakes } ; written by ; linda mcgibney } } ; 2 } = true
Output: select the rows whose directed by record fuzzily matches to jonathan frakes . among these rows , select the rows whose written by record fuzzily matches to linda mcgibney . the number of such rows is 2 .
and { only { filter_less { all_rows ; attendance ; 60000 } } ; eq { hop { filter_less { all_rows ; attendance ; 60000 } ; game site } ; rca dome } } = true
Output: select the rows whose attendance record is less than 60000 . there is only one such row in the table . the game site record of this unqiue row is rca dome .
eq { count { filter_eq { all_rows ; party ; democratic } } ; 2 } = true
Output: select the rows whose party record fuzzily matches to democratic . the number of such rows is 2 .
eq { hop { argmax { all_rows ; deaths } ; year } ; 1867 } = true
Output: select the row whose deaths record of all rows is maximum . the year record of this row is 1867 .
greater { hop { filter_eq { all_rows ; player ; antwaan randle el } ; yards } ; hop { filter_eq { all_rows ; player ; ladell betts } ; yards } } = true
Output: select the rows whose player record fuzzily matches to antwaan randle el . take the yards record of this row . select the rows whose player record fuzzily matches to ladell betts . take the yards record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; studio host ; craig mustard } } ; 5 } = true
Output: select the rows whose studio host record fuzzily matches to craig mustard . the number of such rows is 5 .
and { only { filter_less { all_rows ; crowd ; 10000 } } ; eq { hop { filter_less { all_rows ; crowd ; 10000 } ; home team } ; st kilda } } = true
Output: select the rows whose crowd record is less than 10000 . there is only one such row in the table . the home team record of this unqiue row is st kilda .
round_eq { avg { all_rows ; candidates } ; 65 % } = true
Output: the average of the candidates record of all rows is 65  .
and { only { filter_eq { all_rows ; platform ( s ) ; gamecube } } ; eq { hop { filter_eq { all_rows ; platform ( s ) ; gamecube } ; game } ; resident evil 4 } } = true
Output: select the rows whose platform  s  record fuzzily matches to gamecube . there is only one such row in the table . the game record of this unqiue row is resident evil 4 .
and { only { filter_eq { all_rows ; decision ; johnson } } ; eq { hop { filter_eq { all_rows ; decision ; johnson } ; date } ; november 6 } } = true
Output: select the rows whose decision record fuzzily matches to johnson . there is only one such row in the table . the date record of this unqiue row is november 6 .
only { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60000 } } = true
Output: select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60000 . there is only one such row in the table .
eq { count { filter_eq { all_rows ; competition ; fifa world cup qualification } } ; 2 } = true
Output: select the rows whose competition record fuzzily matches to fifa world cup qualification . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; player ; bill gramatica } ; round } ; hop { filter_eq { all_rows ; player ; renaldo hill } ; round } } = true
Output: select the rows whose player record fuzzily matches to bill gramatica . take the round record of this row . select the rows whose player record fuzzily matches to renaldo hill . take the round record of this row . the first record is less than the second record .
less { hop { filter_eq { all_rows ; singer ; micaela } ; position } ; hop { filter_eq { all_rows ; singer ; serena abrami } ; position } } = true
Output: select the rows whose singer record fuzzily matches to micaela . take the position record of this row . select the rows whose singer record fuzzily matches to serena abrami . take the position record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; points } ; 252.0 } = true
Output: the average of the points record of all rows is 252.0 .
and { only { filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova } } ; eq { hop { filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova } ; date } ; may 4 , 2013 } } = true
Output: select the rows whose opponent record fuzzily matches to anastasia pavlyuchenkova . there is only one such row in the table . the date record of this unqiue row is may 4 , 2013 .
only { filter_eq { all_rows ; nation ; serbia } } = true
Output: select the rows whose nation record fuzzily matches to serbia . there is only one such row in the table .
only { filter_eq { all_rows ; date ; 01/1992 } } = true
Output: select the rows whose date record fuzzily matches to 011992 . there is only one such row in the table .
and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; date } ; 23 october 2005 } } = true
Output: select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the date record of this unqiue row is 23 october 2005 .
and { only { filter_eq { all_rows ; college ; mississippi } } ; eq { hop { filter_eq { all_rows ; college ; mississippi } ; player } ; allen brown } } = true
Output: select the rows whose college record fuzzily matches to mississippi . there is only one such row in the table . the player record of this unqiue row is allen brown .
less { hop { filter_eq { all_rows ; skip ; xu xiaoming } ; season } ; hop { filter_eq { all_rows ; skip ; wang fengchun } ; season } } = true
Output: select the rows whose skip record fuzzily matches to xu xiaoming . take the season record of this row . select the rows whose skip record fuzzily matches to wang fengchun . take the season record of this row . the first record is less than the second record .
most_less { all_rows ; land ( sqmi ) ; 40 } = true
Output: for the land  sqmi  records of all rows , most of them are less than 40 .
eq { count { filter_eq { all_rows ; result ; 5 - 0 } } ; 3 } = true
Output: select the rows whose result record fuzzily matches to 5  0 . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; pick ; 3 } ; player } ; jean - fredric tremblay } = true
Output: select the row whose pick record of all rows is 3rd maximum . the player record of this row is jean  fredric tremblay .
and { greater { hop { filter_eq { all_rows ; episode ; gary marries off his ex } ; air date } ; hop { filter_eq { all_rows ; episode ; gary gets boundaries } ; air date } } ; and { eq { hop { filter_eq { all_rows ; episode ; gary marries off his ex } ; air date } ; october 8 , 2008 } ; eq { hop { filter_eq { all_rows ; episode ; gary gets boundaries } ; air date } ; october 1 , 2008 } } } = true
Output: select the rows whose episode record fuzzily matches to gary marries off his ex . take the air date record of this row . select the rows whose episode record fuzzily matches to gary gets boundaries . take the air date record of this row . the first record is greater than the second record . the air date record of the first row is october 8 , 2008 . the air date record of the second row is october 1 , 2008 .
most_greater_eq { all_rows ; td 's ; 1 } = true
Output: for the td s records of all rows , most of them are greater than or equal to 1 .
eq { hop { argmax { all_rows ; attendance } ; game site } ; los angeles memorial coliseum } = true
Output: select the row whose attendance record of all rows is maximum . the game site record of this row is los angeles memorial coliseum .
eq { hop { nth_argmin { all_rows ; year ; 2 } ; venue } ; warsaw , poland } = true
Output: select the row whose year record of all rows is 2nd minimum . the venue record of this row is warsaw , poland .
most_greater_eq { all_rows ; bodyweight ; 70 } = true
Output: for the bodyweight records of all rows , most of them are greater than or equal to 70 .
eq { count { filter_greater_eq { filter_greater_eq { all_rows ; gold ; 10 } ; silver ; 20 } } ; 3 } = true
Output: select the rows whose gold record is greater than or equal to 10 . among these rows , select the rows whose silver record is greater than or equal to 20 . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; average ; 2 } ; team } ; river plate } = true
Output: select the row whose average record of all rows is 2nd maximum . the team record of this row is river plate .
and { only { filter_greater { all_rows ; al - wehdat wins ; 20 } } ; eq { hop { filter_greater { all_rows ; al - wehdat wins ; 20 } ; tournament } ; jordan premier league } } = true
Output: select the rows whose al  wehdat wins record is greater than 20 . there is only one such row in the table . the tournament record of this unqiue row is jordan premier league .
round_eq { sum { all_rows ; gold } ; 14 } = true
Output: the sum of the gold record of all rows is 14 .
less { hop { filter_eq { all_rows ; rider ; stan woods } ; time } ; hop { filter_eq { all_rows ; rider ; tom loughridge } ; time } } = true
Output: select the rows whose rider record fuzzily matches to stan woods . take the time record of this row . select the rows whose rider record fuzzily matches to tom loughridge . take the time record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; population } ; official name } ; saint george } = true
Output: select the row whose population record of all rows is maximum . the official name record of this row is saint george .
eq { hop { argmax { all_rows ; result } ; date } ; 9 october 2010 } = true
Output: select the row whose result record of all rows is maximum . the date record of this row is 9 october 2010 .
round_eq { avg { all_rows ; attendance } ; 66000 } = true
Output: the average of the attendance record of all rows is 66000 .
eq { hop { nth_argmax { filter_less { all_rows ; area ( km square ) ; 100000 } ; population ; 1 } ; member countries } ; denmark } = true
Output: select the rows whose area  km square  record is less than 100000 . select the row whose population record of these rows is 1st maximum . the member countries record of this row is denmark .
and { eq { nth_min { all_rows ; rank ; 4 } ; 4 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; team } ; estonia } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; wins } ; 2 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; losses } ; 3 } ; eq { hop { nth_argmin { all_rows ; rank ; 4 } ; points } ; 4 } } } } } = true
Output: the 4th minimum rank record of all rows is 4 . the team record of the row with 4th minimum rank record is estonia . the wins record of the row with 4th minimum rank record is 2 . the losses record of the row with 4th minimum rank record is 3 . the points record of the row with 4th minimum rank record is 4 .
round_eq { avg { all_rows ; crowd } ; 16583.33 } = true
Output: the average of the crowd record of all rows is 16583.33 .
all_less { filter_eq { all_rows ; position ; running back } ; round ; 10 } = true
Output: select the rows whose position record fuzzily matches to running back . for the round records of these rows , all of them are less than 10 .
and { only { filter_greater { all_rows ; result ; 50 } } ; and { eq { hop { filter_greater { all_rows ; result ; 50 } ; date } ; september 9 , 1979 } ; eq { hop { filter_greater { all_rows ; result ; 50 } ; opponent } ; cincinnati bengals } } } = true
Output: select the rows whose result record is greater than 50 . there is only one such row in the table . the date record of this unqiue row is september 9 , 1979 . the opponent record of this unqiue row is cincinnati bengals .
and { only { filter_eq { all_rows ; floors ; 1 } } ; eq { hop { filter_eq { all_rows ; floors ; 1 } ; name } ; saint michael 's church } } = true
Output: select the rows whose floors record is equal to 1 . there is only one such row in the table . the name record of this unqiue row is saint michael s church .
eq { hop { argmax { all_rows ; balls } ; player } ; keith boyce } = true
Output: select the row whose balls record of all rows is maximum . the player record of this row is keith boyce .
most_eq { all_rows ; written by ; james wood } = true
Output: for the written by records of all rows , most of them fuzzily match to james wood .
eq { count { filter_eq { all_rows ; won ; 9 } } ; 2 } = true
Output: select the rows whose won record is equal to 9 . the number of such rows is 2 .
and { eq { max { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance } ; 65551 } ; eq { hop { argmax { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance } ; date } ; november 11 , 1979 } } = true
Output: select the rows whose opponent record fuzzily matches to san francisco 49ers . the maximum attendance record of these rows is 65551 . the date record of the row with superlative attendance record is november 11 , 1979 .
eq { hop { nth_argmax { all_rows ; season ; 1 } ; name } ; bakari hendrix } = true
Output: select the row whose season record of all rows is 1st maximum . the name record of this row is bakari hendrix .
eq { count { filter_greater { all_rows ; population ( 1991 ) ; 15,000 } } ; 4 } = true
Output: select the rows whose population  1991  record is greater than 15,000 . the number of such rows is 4 .
round_eq { avg { all_rows ; roll } ; 99.61 } = true
Output: the average of the roll record of all rows is 99.61 .
eq { hop { nth_argmax { all_rows ; uk viewers ( million ) ; 5 } ; title } ; episode 4 } = true
Output: select the row whose uk viewers  million  record of all rows is 5th maximum . the title record of this row is episode 4 .