niryuu/sni-each-converted huggingface dataset:
eq { hop { argmax { all_rows ; attendance } ; date } ; october 22 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is october 22 .
and { eq { nth_min { filter_eq { all_rows ; format ; cd } ; date ; 1 } ; september 10 , 1986 } ; eq { hop { nth_argmin { filter_eq { all_rows ; format ; cd } ; date ; 1 } ; label } ; alfa records } } = true
Output: select the rows whose format record fuzzily matches to cd . the 1st minimum date record of these rows is september 10 , 1986 . the label record of the row with 1st minimum date record is alfa records .
round_eq { avg { all_rows ; decile } ; 5 } = true
Output: the average of the decile record of all rows is 5 .
round_eq { sum { all_rows ; total } ; 18 } = true
Output: the sum of the total record of all rows is 18 .
eq { count { filter_eq { all_rows ; party ; democratic } } ; 5 } = true
Output: select the rows whose party record fuzzily matches to democratic . the number of such rows is 5 .
eq { count { filter_eq { all_rows ; status ; re - elected } } ; 3 } = true
Output: select the rows whose status record fuzzily matches to re  elected . the number of such rows is 3 .
eq { hop { filter_eq { all_rows ; class ; a - 4 } ; quantity made } ; hop { filter_eq { all_rows ; class ; a - 3 } ; quantity made } } = true
Output: select the rows whose class record fuzzily matches to a  4 . take the quantity made record of this row . select the rows whose class record fuzzily matches to a  3 . take the quantity made record of this row . the first record is equal to the second record .
and { only { filter_eq { all_rows ; chassis - engine ; jaguar xjr - 8 } } ; eq { hop { filter_eq { all_rows ; chassis - engine ; jaguar xjr - 8 } ; driver } ; raul boesel } } = true
Output: select the rows whose chassis  engine record fuzzily matches to jaguar xjr  8 . there is only one such row in the table . the driver record of this unqiue row is raul boesel .
and { only { filter_eq { all_rows ; unwto region ; oceania } } ; eq { hop { filter_eq { all_rows ; unwto region ; oceania } ; country } ; australia } } = true
Output: select the rows whose unwto region record fuzzily matches to oceania . there is only one such row in the table . the country record of this unqiue row is australia .
and { only { filter_less { filter_eq { all_rows ; hometown ; santiago de los caballeros } ; age ; 19 } } ; eq { hop { filter_less { filter_eq { all_rows ; hometown ; santiago de los caballeros } ; age ; 19 } ; contestant } ; valerie chardonnens vargas } } = true
Output: select the rows whose hometown record fuzzily matches to santiago de los caballeros . among these rows , select the rows whose age record is less than 19 . there is only one such row in the table . the contestant record of this unqiue row is valerie chardonnens vargas .
all_greater { all_rows ; closed ; 2005 } = true
Output: for the closed records of all rows , all of them are greater than 2005 .
eq { hop { argmax { all_rows ; enrollment } ; institution } ; grove city college } = true
Output: select the row whose enrollment record of all rows is maximum . the institution record of this row is grove city college .
and { only { filter_eq { all_rows ; nationality ; milanese } } ; eq { hop { filter_eq { all_rows ; nationality ; milanese } ; elector } ; simone borsano } } = true
Output: select the rows whose nationality record fuzzily matches to milanese . there is only one such row in the table . the elector record of this unqiue row is simone borsano .
greater { hop { filter_eq { all_rows ; year ; 2005 } ; money ( ￥ ) } ; hop { filter_eq { all_rows ; year ; 2006 } ; money ( ￥ ) } } = true
Output: select the rows whose year record fuzzily matches to 2005 . take the money  ￥  record of this row . select the rows whose year record fuzzily matches to 2006 . take the money  ￥  record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; builder ; hunslet } } ; 4 } = true
Output: select the rows whose builder record fuzzily matches to hunslet . the number of such rows is 4 .
eq { hop { argmax { all_rows ; height feet / metres } ; name } ; dravska vrata } = true
Output: select the row whose height feet  metres record of all rows is maximum . the name record of this row is dravska vrata .
eq { hop { argmax { all_rows ; caps } ; name } ; vaughan coveny } = true
Output: select the row whose caps record of all rows is maximum . the name record of this row is vaughan coveny .
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; season } ; 1993 } = true
Output: select the row whose attendance record of all rows is 1st maximum . the season record of this row is 1993 .
greater { hop { filter_eq { all_rows ; agent ; arsine } ; effectiveness as blood agent } ; hop { filter_eq { all_rows ; agent ; vinyl arsine } ; effectiveness as blood agent } } = true
Output: select the rows whose agent record fuzzily matches to arsine . take the effectiveness as blood agent record of this row . select the rows whose agent record fuzzily matches to vinyl arsine . take the effectiveness as blood agent record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; tv network ; cbs } } ; 12 } = true
Output: select the rows whose tv network record fuzzily matches to cbs . the number of such rows is 12 .
all_eq { all_rows ; gfl premierships ; nil } = true
Output: for the gfl premierships records of all rows , all of them fuzzily match to nil .
and { only { filter_eq { all_rows ; surface ; clay } } ; and { eq { hop { filter_eq { all_rows ; surface ; clay } ; year } ; 1994 } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; championship } ; rome } } } = true
Output: select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the year record of this unqiue row is 1994 . the championship record of this unqiue row is rome .
and { only { filter_less { all_rows ; laps ; 5 } } ; eq { hop { filter_less { all_rows ; laps ; 5 } ; year } ; 1953 } } = true
Output: select the rows whose laps record is less than 5 . there is only one such row in the table . the year record of this unqiue row is 1953 .
less { hop { filter_eq { all_rows ; tournament ; baton rouge , us } ; date } ; hop { filter_eq { all_rows ; tournament ; granby , canada } ; date } } = true
Output: select the rows whose tournament record fuzzily matches to baton rouge , us . take the date record of this row . select the rows whose tournament record fuzzily matches to granby , canada . take the date record of this row . the first record is less than the second record .
eq { hop { argmin { all_rows ; date } ; venue } ; corio oval } = true
Output: select the row whose date record of all rows is minimum . the venue record of this row is corio oval .
eq { hop { argmin { filter_eq { all_rows ; label ; b ' in music } ; release date } ; english title } ; kissing the future of love } = true
Output: select the rows whose label record fuzzily matches to b  in music . select the row whose release date record of these rows is minimum . the english title record of this row is kissing the future of love .
eq { hop { argmax { all_rows ; away team score } ; away team } ; footscray } = true
Output: select the row whose away team score record of all rows is maximum . the away team record of this row is footscray .
eq { hop { nth_argmin { all_rows ; date ; 3 } ; number & name } ; no46233 duchess of sutherland } = true
Output: select the row whose date record of all rows is 3rd minimum . the number  name record of this row is no46233 duchess of sutherland .
eq { hop { argmin { all_rows ; stage } ; year } ; 2013 } = true
Output: select the row whose stage record of all rows is minimum . the year record of this row is 2013 .
most_eq { filter_less { all_rows ; date ; january 1 , 2000 } ; format ; cd } = true
Output: select the rows whose date record is less than january 1 , 2000 . for the format records of these rows , most of them fuzzily match to cd .
eq { count { filter_all { all_rows ; region } } ; 5 } = true
Output: select the rows whose region record is arbitrary . the number of such rows is 5 .
most_eq { all_rows ; sport ; football } = true
Output: for the sport records of all rows , most of them fuzzily match to football .
all_eq { all_rows ; date ; 19 april 1975 } = true
Output: for the date records of all rows , all of them fuzzily match to 19 april 1975 .
and { only { filter_eq { all_rows ; college ; notre dame } } ; eq { hop { filter_eq { all_rows ; college ; notre dame } ; player } ; rodney culver } } = true
Output: select the rows whose college record fuzzily matches to notre dame . there is only one such row in the table . the player record of this unqiue row is rodney culver .
and { eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; hop { filter_eq { all_rows ; week ; top 10 } ; result } } ; and { eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; bottom 2 } ; eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; bottom 2 } } } = true
Output: select the rows whose week record fuzzily matches to top 10 . take the result record of this row . select the rows whose week record fuzzily matches to top 10 . take the result record of this row . the first record fuzzily matches to the second record . the result record of the first row is bottom 2 . the result record of the second row is bottom 2 .
eq { hop { nth_argmin { all_rows ; max power at rpm ; 2 } ; engine name } ; 1.2 tsi } = true
Output: select the row whose max power at rpm record of all rows is 2nd minimum . the engine name record of this row is 1.2 tsi .
eq { count { filter_eq { all_rows ; host ; kettering } } ; 2 } = true
Output: select the rows whose host record fuzzily matches to kettering . the number of such rows is 2 .
and { only { filter_eq { all_rows ; agricultural panel ; 0 } } ; eq { hop { filter_eq { all_rows ; agricultural panel ; 0 } ; party } ; independent } } = true
Output: select the rows whose agricultural panel record is equal to 0 . there is only one such row in the table . the party record of this unqiue row is independent .
round_eq { avg { all_rows ; height in ft } ; 6 - 9 } = true
Output: the average of the height in ft record of all rows is 6  9 .
eq { hop { nth_argmax { all_rows ; wins ; 2 } ; club } ; etoile sportive du sahel ( ess ) } = true
Output: select the row whose wins record of all rows is 2nd maximum . the club record of this row is etoile sportive du sahel  ess  .
and { only { filter_eq { all_rows ; group position ; 2nd } } ; eq { hop { filter_eq { all_rows ; group position ; 2nd } ; date } ; 18 september 2002 } } = true
Output: select the rows whose group position record fuzzily matches to 2nd . there is only one such row in the table . the date record of this unqiue row is 18 september 2002 .
most_eq { all_rows ; ground ; humber college north } = true
Output: for the ground records of all rows , most of them fuzzily match to humber college north .
round_eq { avg { all_rows ; crowd } ; 13841 } = true
Output: the average of the crowd record of all rows is 13841 .
eq { count { filter_eq { all_rows ; result ; w } } ; 10 } = true
Output: select the rows whose result record fuzzily matches to w . the number of such rows is 10 .
eq { count { filter_all { all_rows ; area served } } ; 6 } = true
Output: select the rows whose area served record is arbitrary . the number of such rows is 6 .
eq { count { filter_all { all_rows ; series } } ; 7 } = true
Output: select the rows whose series record is arbitrary . the number of such rows is 7 .
eq { diff { hop { filter_eq { all_rows ; name ; niki cross } ; minutes } ; hop { filter_eq { all_rows ; name ; amanda cinalli } ; minutes } } ; -60 } = true
Output: select the rows whose name record fuzzily matches to niki cross . take the minutes record of this row . select the rows whose name record fuzzily matches to amanda cinalli . take the minutes record of this row . the second record is 60 larger than the first record .
and { only { filter_eq { all_rows ; time / retired ; accident } } ; eq { hop { filter_eq { all_rows ; time / retired ; accident } ; driver } ; jon herb } } = true
Output: select the rows whose time  retired record fuzzily matches to accident . there is only one such row in the table . the driver record of this unqiue row is jon herb .
round_eq { avg { all_rows ; population urban area in millions } ; 5.1 } = true
Output: the average of the population urban area in millions record of all rows is 5.1 .
round_eq { avg { all_rows ; group 's equity shareholding } ; 85 % } = true
Output: the average of the group s equity shareholding record of all rows is 85  .
eq { hop { nth_argmin { all_rows ; black knights points ; 4 } ; date } ; oct 20 } = true
Output: select the row whose black knights points record of all rows is 4th minimum . the date record of this row is oct 20 .
eq { count { filter_eq { all_rows ; opponent ; thomas muster } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to thomas muster . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; current club ; panathinaikos } } ; 5 } = true
Output: select the rows whose current club record fuzzily matches to panathinaikos . the number of such rows is 5 .
round_eq { avg { all_rows ; attendance } ; 43174 } = true
Output: the average of the attendance record of all rows is 43174 .
eq { count { filter_eq { all_rows ; outcome ; runner - up } } ; 6 } = true
Output: select the rows whose outcome record fuzzily matches to runner  up . the number of such rows is 6 .
greater { hop { filter_eq { all_rows ; house name ; gongola } ; founded } ; hop { filter_eq { all_rows ; house name ; benue } ; founded } } = true
Output: select the rows whose house name record fuzzily matches to gongola . take the founded record of this row . select the rows whose house name record fuzzily matches to benue . take the founded record of this row . the first record is greater than the second record .
round_eq { sum { filter_eq { all_rows ; team ; usa } ; final } ; 15:49.38 } = true
Output: select the rows whose team record fuzzily matches to usa . the sum of the final record of these rows is 15:49.38 .
eq { hop { argmin { filter_eq { all_rows ; opponents ; palamós } ; kick off } ; referee } ; daudén ibáñez } = true
Output: select the rows whose opponents record fuzzily matches to palamós . select the row whose kick off record of these rows is minimum . the referee record of this row is daudén ibáñez .
greater { hop { filter_eq { all_rows ; opponent ; petr kelner } ; round } ; hop { filter_eq { all_rows ; opponent ; andrej bregar } ; round } } = true
Output: select the rows whose opponent record fuzzily matches to petr kelner . take the round record of this row . select the rows whose opponent record fuzzily matches to andrej bregar . take the round record of this row . the first record is greater than the second record .
eq { count { filter_greater { all_rows ; fog ( days / year ) ; 30 } } ; 4 } = true
Output: select the rows whose fog  days  year  record is greater than 30 . the number of such rows is 4 .
and { only { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } } ; and { eq { hop { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } ; home team } ; richmond } ; eq { hop { filter_greater { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } ; away team } ; carlton } } } = true
Output: select the rows whose date record fuzzily matches to 12 june 1937 . among these rows , select the rows whose crowd record is greater than 20000 . there is only one such row in the table . the home team record of this unqiue row is richmond . the away team record of this unqiue row is carlton .
greater { hop { filter_eq { all_rows ; year ; 1968 } ; pts } ; hop { filter_eq { all_rows ; year ; 1973 } ; pts } } = true
Output: select the rows whose year record fuzzily matches to 1968 . take the pts record of this row . select the rows whose year record fuzzily matches to 1973 . take the pts record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; laps } ; 164 } = true
Output: the average of the laps record of all rows is 164 .
eq { count { filter_eq { all_rows ; status ; re - elected } } ; 10 } = true
Output: select the rows whose status record fuzzily matches to re  elected . the number of such rows is 10 .
round_eq { sum { filter_greater_eq { all_rows ; week ; 13 } ; attendance } ; 95,901 } = true
Output: select the rows whose week record is greater than or equal to 13 . the sum of the attendance record of these rows is 95,901 .
eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; victoria park } = true
Output: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is victoria park .
eq { hop { argmax { all_rows ; total } ; shooter } ; ariel mauricio flores ( mex ) } = true
Output: select the row whose total record of all rows is maximum . the shooter record of this row is ariel mauricio flores  mex  .
round_eq { avg { all_rows ; enrollment } ; 2030 } = true
Output: the average of the enrollment record of all rows is 2030 .
eq { hop { nth_argmin { all_rows ; time ; 2 } ; name } ; pieter van den hoogenband } = true
Output: select the row whose time record of all rows is 2nd minimum . the name record of this row is pieter van den hoogenband .
all_eq { all_rows ; played ; 38 } = true
Output: for the played records of all rows , all of them are equal to 38 .
and { only { filter_greater { all_rows ; speed ; 18.00 km/h } } ; eq { hop { filter_greater { all_rows ; speed ; 18.00 km/h } ; rider } ; roberto heras ( esp ) } } = true
Output: select the rows whose speed record is greater than 18.00 kmh . there is only one such row in the table . the rider record of this unqiue row is roberto heras  esp  .
eq { hop { argmax { all_rows ; metacritic } ; game title } ; sly 2 : band of thieves } = true
Output: select the row whose metacritic record of all rows is maximum . the game title record of this row is sly 2 : band of thieves .
less { hop { filter_eq { all_rows ; election ; 2004 } ; share of votes } ; hop { filter_eq { all_rows ; election ; 1996 } ; share of votes } } = true
Output: select the rows whose election record fuzzily matches to 2004 . take the share of votes record of this row . select the rows whose election record fuzzily matches to 1996 . take the share of votes record of this row . the first record is less than the second record .
eq { hop { nth_argmax { all_rows ; year ( ceremony ) ; 3 } ; english title } ; the wind journeys } = true
Output: select the row whose year  ceremony  record of all rows is 3rd maximum . the english title record of this row is the wind journeys .
most_eq { filter_eq { all_rows ; player ; mike hercus } ; tries ; 0 } = true
Output: select the rows whose player record fuzzily matches to mike hercus . for the tries records of these rows , most of them are equal to 0 .
eq { hop { nth_argmin { all_rows ; year born ; 1 } ; player } ; sacha giffa } = true
Output: select the row whose year born record of all rows is 1st minimum . the player record of this row is sacha giffa .
less { hop { filter_eq { all_rows ; format ; christian } ; frequency } ; hop { filter_eq { all_rows ; format ; public broadcasting } ; frequency } } = true
Output: select the rows whose format record fuzzily matches to christian . take the frequency record of this row . select the rows whose format record fuzzily matches to public broadcasting . take the frequency record of this row . the first record is less than the second record .
only { filter_eq { all_rows ; category ; best supporting actress } } = true
Output: select the rows whose category record fuzzily matches to best supporting actress . there is only one such row in the table .
and { only { filter_less { all_rows ; of seats won ; 10 } } ; eq { hop { filter_less { all_rows ; of seats won ; 10 } ; election } ; 1993 } } = true
Output: select the rows whose of seats won record is less than 10 . there is only one such row in the table . the election record of this unqiue row is 1993 .
eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; lake oval } = true
Output: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is lake oval .
most_eq { all_rows ; court surface ; hard } = true
Output: for the court surface records of all rows , most of them fuzzily match to hard .
most_eq { all_rows ; socket ; lga 1156 } = true
Output: for the socket records of all rows , most of them fuzzily match to lga 1156 .
round_eq { avg { all_rows ; points } ; 14.56 } = true
Output: the average of the points record of all rows is 14.56 .
eq { count { filter_eq { filter_eq { all_rows ; congress ; 111th congress } ; date introduced ; april } } ; 2 } = true
Output: select the rows whose congress record fuzzily matches to 111th congress . among these rows , select the rows whose date introduced record fuzzily matches to april . the number of such rows is 2 .
eq { hop { argmin { all_rows ; population } ; english name } ; zhangping city } = true
Output: select the row whose population record of all rows is minimum . the english name record of this row is zhangping city .
round_eq { avg { all_rows ; points } ; 30 } = true
Output: the average of the points record of all rows is 30 .
and { eq { hop { filter_eq { all_rows ; award ; laurence olivier award } ; category } ; hop { filter_eq { all_rows ; award ; theatregoers ' choice award } ; category } } ; and { eq { hop { filter_eq { all_rows ; award ; laurence olivier award } ; category } ; best actress in a musical } ; eq { hop { filter_eq { all_rows ; award ; theatregoers ' choice award } ; category } ; best actress in a musical } } } = true
Output: select the rows whose award record fuzzily matches to laurence olivier award . take the category record of this row . select the rows whose award record fuzzily matches to theatregoers  choice award . take the category record of this row . the first record fuzzily matches to the second record . the category record of the first row is best actress in a musical . the category record of the second row is best actress in a musical .
eq { hop { nth_argmax { all_rows ; frequency mhz ; 2 } ; call sign } ; w291aq } = true
Output: select the row whose frequency mhz record of all rows is 2nd maximum . the call sign record of this row is w291aq .
greater { hop { filter_eq { all_rows ; date ; 29 november 2007 } ; attendance } ; hop { filter_eq { all_rows ; date ; 30 november 2007 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to 29 november 2007 . take the attendance record of this row . select the rows whose date record fuzzily matches to 30 november 2007 . take the attendance record of this row . the first record is greater than the second record .
all_eq { all_rows ; engine ; offenhauser l4 } = true
Output: for the engine records of all rows , all of them fuzzily match to offenhauser l4 .
eq { count { filter_eq { all_rows ; year joined ; 1953 } } ; 4 } = true
Output: select the rows whose year joined record is equal to 1953 . the number of such rows is 4 .
eq { hop { nth_argmax { all_rows ; total ; 3 } ; nation } ; mongolia } = true
Output: select the row whose total record of all rows is 3rd maximum . the nation record of this row is mongolia .
eq { count { filter_less { filter_greater { all_rows ; home team score ; 12 } ; crowd ; 20000 } } ; 2 } = true
Output: select the rows whose home team score record is greater than 12 . among these rows , select the rows whose crowd record is less than 20000 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; first place } ; region represented } ; china } = true
Output: select the row whose first place record of all rows is maximum . the region represented record of this row is china .
eq { hop { nth_argmax { all_rows ; density ( pop per km2 ) ; 2 } ; regional county municipality ( rcm ) } ; la haute - yamaska } = true
Output: select the row whose density  pop per km2  record of all rows is 2nd maximum . the regional county municipality  rcm  record of this row is la haute  yamaska .
and { only { filter_eq { all_rows ; gold ; 4 } } ; eq { hop { filter_eq { all_rows ; gold ; 4 } ; nation } ; south korea } } = true
Output: select the rows whose gold record is equal to 4 . there is only one such row in the table . the nation record of this unqiue row is south korea .
round_eq { avg { all_rows ; size } ; 405.1 } = true
Output: the average of the size record of all rows is 405.1 .
only { filter_eq { all_rows ; venue ; china } } = true
Output: select the rows whose venue record fuzzily matches to china . there is only one such row in the table .
eq { count { filter_greater { filter_eq { all_rows ; country ; united states } ; score ; 67 } } ; 5 } = true
Output: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record is greater than 67 . the number of such rows is 5 .
eq { hop { nth_argmax { all_rows ; team number ; 3 } ; team name } ; sap g33k } = true
Output: select the row whose team number record of all rows is 3rd maximum . the team name record of this row is sap g33k .
eq { count { filter_all { all_rows ; round } } ; 7 } = true
Output: select the rows whose round record is arbitrary . the number of such rows is 7 .
and { only { filter_eq { all_rows ; points ; 24 } } ; eq { hop { filter_eq { all_rows ; points ; 24 } ; november } ; 20 } } = true
Output: select the rows whose points record is equal to 24 . there is only one such row in the table . the november record of this unqiue row is 20 .
most_eq { all_rows ; lost ; 5 } = true
Output: for the lost records of all rows , most of them are equal to 5 .
most_eq { all_rows ; date ; april } = true
Output: for the date records of all rows , most of them fuzzily match to april .
eq { hop { argmax { all_rows ; run time } ; episode number } ; 313 } = true
Output: select the row whose run time record of all rows is maximum . the episode number record of this row is 313 .
round_eq { avg { all_rows ; capacity } ; 1732 } = true
Output: the average of the capacity record of all rows is 1732 .
eq { count { filter_eq { all_rows ; team ; forsythe racing } } ; 3 } = true
Output: select the rows whose team record fuzzily matches to forsythe racing . the number of such rows is 3 .
eq { count { filter_all { all_rows ; episode } } ; 13 } = true
Output: select the rows whose episode record is arbitrary . the number of such rows is 13 .
eq { hop { argmax { all_rows ; gold } ; nation } ; soviet union } = true
Output: select the row whose gold record of all rows is maximum . the nation record of this row is soviet union .
eq { hop { argmin { all_rows ; us air date } ; storyteller } ; kristen } = true
Output: select the row whose us air date record of all rows is minimum . the storyteller record of this row is kristen .
most_eq { filter_eq { all_rows ; dutch cup ; finalist } ; postseason ; champion } = true
Output: select the rows whose dutch cup record fuzzily matches to finalist . for the postseason records of these rows , most of them fuzzily match to champion .
and { eq { hop { filter_eq { all_rows ; year ; 1992 } ; avg finish } ; hop { filter_eq { all_rows ; year ; 1994 } ; avg finish } } ; and { eq { hop { filter_eq { all_rows ; year ; 1992 } ; avg finish } ; 28.0 } ; eq { hop { filter_eq { all_rows ; year ; 1994 } ; avg finish } ; 28.0 } } } = true
Output: select the rows whose year record fuzzily matches to 1992 . take the avg finish record of this row . select the rows whose year record fuzzily matches to 1994 . take the avg finish record of this row . the first record is equal to the second record . the avg finish record of the first row is 28.0 . the avg finish record of the second row is 28.0 .
eq { count { filter_eq { all_rows ; h / a ; a } } ; 3 } = true
Output: select the rows whose h  a record fuzzily matches to a . the number of such rows is 3 .
eq { hop { argmax { all_rows ; number of seasons in liga mx } ; club } ; américa } = true
Output: select the row whose number of seasons in liga mx record of all rows is maximum . the club record of this row is américa .
most_eq { all_rows ; most wkts ; george davidson } = true
Output: for the most wkts records of all rows , most of them fuzzily match to george davidson .
eq { count { filter_eq { all_rows ; winning driver ; harry schell } } ; 2 } = true
Output: select the rows whose winning driver record fuzzily matches to harry schell . the number of such rows is 2 .
eq { hop { nth_argmin { all_rows ; founded ; 3 } ; institution } ; university of california , davis } = true
Output: select the row whose founded record of all rows is 3rd minimum . the institution record of this row is university of california , davis .
eq { hop { nth_argmin { all_rows ; 1980 ; 2 } ; greek national account } ; public debt 8 ( billion ) } = true
Output: select the row whose 1980 record of all rows is 2nd minimum . the greek national account record of this row is public debt 8  billion  .
eq { count { filter_eq { all_rows ; season outcome ; div i playoffs } } ; 2 } = true
Output: select the rows whose season outcome record fuzzily matches to div i playoffs . the number of such rows is 2 .
most_eq { all_rows ; builder ; avonside engine company } = true
Output: for the builder records of all rows , most of them fuzzily match to avonside engine company .
and { only { filter_greater { all_rows ; points ; 41 } } ; eq { hop { filter_greater { all_rows ; points ; 41 } ; artist } ; extra nena } } = true
Output: select the rows whose points record is greater than 41 . there is only one such row in the table . the artist record of this unqiue row is extra nena .
greater { hop { filter_eq { all_rows ; opponent ; slough jets } ; attendance } ; hop { filter_eq { all_rows ; opponent ; sheffield scimitars } ; attendance } } = true
Output: select the rows whose opponent record fuzzily matches to slough jets . take the attendance record of this row . select the rows whose opponent record fuzzily matches to sheffield scimitars . take the attendance record of this row . the first record is greater than the second record .
most_eq { all_rows ; county ; 12 clinton } = true
Output: for the county records of all rows , most of them fuzzily match to 12 clinton .
eq { count { filter_eq { all_rows ; location ; winnipeg , canada } } ; 2 } = true
Output: select the rows whose location record fuzzily matches to winnipeg , canada . the number of such rows is 2 .
eq { count { filter_greater { all_rows ; capacity ; 5000 } } ; 11 } = true
Output: select the rows whose capacity record is greater than 5000 . the number of such rows is 11 .
most_eq { all_rows ; high assists ; mike conley } = true
Output: for the high assists records of all rows , most of them fuzzily match to mike conley .
greater { hop { filter_eq { all_rows ; home team ; manchester city } ; score } ; hop { filter_eq { all_rows ; home team ; arsenal } ; score } } = true
Output: select the rows whose home team record fuzzily matches to manchester city . take the score record of this row . select the rows whose home team record fuzzily matches to arsenal . take the score record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; name ; mike gann } ; round } ; hop { filter_eq { all_rows ; name ; brent martin } ; round } } = true
Output: select the rows whose name record fuzzily matches to mike gann . take the round record of this row . select the rows whose name record fuzzily matches to brent martin . take the round record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; finish position } ; 29.8 } = true
Output: the average of the finish position record of all rows is 29.8 .
eq { count { filter_eq { all_rows ; matches ; 10 } } ; 2 } = true
Output: select the rows whose matches record is equal to 10 . the number of such rows is 2 .
all_eq { all_rows ; played ; 30 } = true
Output: for the played records of all rows , all of them are equal to 30 .
eq { count { filter_less { all_rows ; age ; 25 } } ; 2 } = true
Output: select the rows whose age record is less than 25 . the number of such rows is 2 .
round_eq { avg { all_rows ; score } ; 143 } = true
Output: the average of the score record of all rows is 143 .
and { only { filter_eq { all_rows ; surface ; grass } } ; eq { hop { filter_eq { all_rows ; surface ; grass } ; outcome } ; winner } } = true
Output: select the rows whose surface record fuzzily matches to grass . there is only one such row in the table . the outcome record of this unqiue row is winner .
round_eq { avg { all_rows ; attendance } ; 58260 } = true
Output: the average of the attendance record of all rows is 58260 .
round_eq { avg { all_rows ; tests won by england } ; 0.47 } = true
Output: the average of the tests won by england record of all rows is 0.47 .
greater { hop { filter_eq { all_rows ; election ; 1988 } ; of total votes } ; hop { filter_eq { all_rows ; election ; 1993 } ; of total votes } } = true
Output: select the rows whose election record fuzzily matches to 1988 . take the of total votes record of this row . select the rows whose election record fuzzily matches to 1993 . take the of total votes record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; institution ; anglo irish bank } ; tier 1 capital , million } ; hop { filter_eq { all_rows ; institution ; irish life and permanent } ; tier 1 capital , million } } = true
Output: select the rows whose institution record fuzzily matches to anglo irish bank . take the tier 1 capital , million record of this row . select the rows whose institution record fuzzily matches to irish life and permanent . take the tier 1 capital , million record of this row . the first record is greater than the second record .
most_not_eq { all_rows ; agg ; 0 } = true
Output: for the agg records of all rows , most of them are not equal to 0 .
and { only { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } } ; eq { hop { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } ; team } ; wanda nowa huta } } = true
Output: select the rows whose points record is less than 20 . among these rows , select the rows whose draw record is equal to 2 . there is only one such row in the table . the team record of this unqiue row is wanda nowa huta .
less { hop { filter_eq { all_rows ; incumbent ; wilbur mills } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; james william trimble } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to wilbur mills . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to james william trimble . take the first elected record of this row . the first record is less than the second record .
most_eq { all_rows ; laps ; 24 } = true
Output: for the laps records of all rows , most of them are equal to 24 .
and { only { filter_eq { filter_greater { all_rows ; starts ; 30 } ; losses ; 17 } } ; eq { hop { filter_eq { filter_greater { all_rows ; starts ; 30 } ; losses ; 17 } ; name } ; brady quinn } } = true
Output: select the rows whose starts record is greater than 30 . among these rows , select the rows whose losses record is equal to 17 . there is only one such row in the table . the name record of this unqiue row is brady quinn .
most_eq { filter_less { all_rows ; points ; 10 } ; difference ; - } = true
Output: select the rows whose points record is less than 10 . for the difference records of these rows , most of them fuzzily match to  .
and { only { filter_eq { filter_eq { all_rows ; supercopa sudamericana 1996 ; did not qualify } ; copa conmebol 1996 ; semifinals } } ; and { eq { hop { filter_eq { filter_eq { all_rows ; supercopa sudamericana 1996 ; did not qualify } ; copa conmebol 1996 ; semifinals } ; team } ; vasco } ; eq { hop { filter_eq { filter_eq { all_rows ; supercopa sudamericana 1996 ; did not qualify } ; copa conmebol 1996 ; semifinals } ; copa libertadores 1996 } ; did not qualify } } } = true
Output: select the rows whose supercopa sudamericana 1996 record fuzzily matches to did not qualify . among these rows , select the rows whose copa conmebol 1996 record fuzzily matches to semifinals . there is only one such row in the table . the team record of this unqiue row is vasco . the copa libertadores 1996 record of this unqiue row is did not qualify .
eq { count { filter_eq { all_rows ; champion ; spartak moscow } } ; 7 } = true
Output: select the rows whose champion record fuzzily matches to spartak moscow . the number of such rows is 7 .
greater { hop { filter_eq { all_rows ; film ; furnace four } ; award } ; hop { filter_eq { all_rows ; film ; the hardest part } ; award } } = true
Output: select the rows whose film record fuzzily matches to furnace four . take the award record of this row . select the rows whose film record fuzzily matches to the hardest part . take the award record of this row . the first record is greater than the second record .
only { filter_eq { all_rows ; location ; poland } } = true
Output: select the rows whose location record fuzzily matches to poland . there is only one such row in the table .
eq { count { filter_eq { all_rows ; men 's singles ; tan joe hok } } ; 2 } = true
Output: select the rows whose men s singles record fuzzily matches to tan joe hok . the number of such rows is 2 .
all_eq { all_rows ; nationality ; united states } = true
Output: for the nationality records of all rows , all of them fuzzily match to united states .
eq { count { filter_eq { all_rows ; reason for change ; resigned } } ; 3 } = true
Output: select the rows whose reason for change record fuzzily matches to resigned . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; capacity ; 2 } ; stadium } ; somerset park } = true
Output: select the row whose capacity record of all rows is 2nd maximum . the stadium record of this row is somerset park .
most_eq { filter_eq { all_rows ; date ; august } ; result ; l } = true
Output: select the rows whose date record fuzzily matches to august . for the result records of these rows , most of them fuzzily match to l .
less { hop { filter_eq { all_rows ; country ; australia } ; time } ; hop { filter_eq { all_rows ; country ; united states } ; time } } = true
Output: select the rows whose country record fuzzily matches to australia . take the time record of this row . select the rows whose country record fuzzily matches to united states . take the time record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; written by ; danielle evenson } } ; eq { hop { filter_eq { all_rows ; written by ; danielle evenson } ; title } ; long days journey into stan } } = true
Output: select the rows whose written by record fuzzily matches to danielle evenson . there is only one such row in the table . the title record of this unqiue row is long days journey into stan .
eq { min { filter_eq { all_rows ; manufacturer ; alco - schenectady } ; year made } ; 1904 - 1907 } = true
Output: select the rows whose manufacturer record fuzzily matches to alco  schenectady . the minimum year made record of these rows is 1904  1907 .
eq { count { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 70000 } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 70000 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; score } ; date } ; 23 may 2002 } = true
Output: select the row whose score record of all rows is maximum . the date record of this row is 23 may 2002 .
round_eq { sum { all_rows ; score } ; 8 } = true
Output: the sum of the score record of all rows is 8 .
eq { count { filter_eq { all_rows ; class ; nuttall } } ; 2 } = true
Output: select the rows whose class record fuzzily matches to nuttall . the number of such rows is 2 .
round_eq { avg { all_rows ; wins } ; 15 } = true
Output: the average of the wins record of all rows is 15 .
and { eq { hop { filter_eq { all_rows ; republican ticket ; frederick e crane } ; democratic ticket } ; hop { filter_eq { all_rows ; republican ticket ; frederick e crane } ; democratic ticket } } ; and { eq { hop { filter_eq { all_rows ; republican ticket ; frederick e crane } ; democratic ticket } ; frederick e crane } ; eq { hop { filter_eq { all_rows ; republican ticket ; frederick e crane } ; democratic ticket } ; frederick e crane } } } = true
Output: select the rows whose republican ticket record fuzzily matches to frederick e crane . take the democratic ticket record of this row . select the rows whose republican ticket record fuzzily matches to frederick e crane . take the democratic ticket record of this row . the first record fuzzily matches to the second record . the democratic ticket record of the first row is frederick e crane . the democratic ticket record of the second row is frederick e crane .
eq { hop { argmax { all_rows ; crowd } ; venue } ; victoria park } = true
Output: select the row whose crowd record of all rows is maximum . the venue record of this row is victoria park .
eq { hop { nth_argmax { all_rows ; f bolt ( kgf ) ; 2 } ; chambering } ; .300 lapua magnum } = true
Output: select the row whose f bolt  kgf  record of all rows is 2nd maximum . the chambering record of this row is .300 lapua magnum .
most_eq { all_rows ; result ; w } = true
Output: for the result records of all rows , most of them fuzzily match to w .
eq { hop { argmin { all_rows ; rank } ; candidate 's name } ; michael mackay } = true
Output: select the row whose rank record of all rows is minimum . the candidate s name record of this row is michael mackay .
greater { hop { filter_eq { all_rows ; currency ; argentine peso ( ars ) } ; 1 usd = } ; hop { filter_eq { all_rows ; currency ; brazilian real ( brl ) } ; 1 usd = } } = true
Output: select the rows whose currency record fuzzily matches to argentine peso  ars  . take the 1 usd = record of this row . select the rows whose currency record fuzzily matches to brazilian real  brl  . take the 1 usd = record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; laps ; 2 } ; year } ; 2003 } = true
Output: select the row whose laps record of all rows is 2nd maximum . the year record of this row is 2003 .
eq { count { filter_eq { all_rows ; nba years ; 3 } } ; 5 } = true
Output: select the rows whose nba years record is equal to 3 . the number of such rows is 5 .
round_eq { avg { all_rows ; pts } ; 2.33 } = true
Output: the average of the pts record of all rows is 2.33 .
eq { hop { argmax { all_rows ; attendance } ; opponent } ; melbourne storm } = true
Output: select the row whose attendance record of all rows is maximum . the opponent record of this row is melbourne storm .
eq { hop { argmax { all_rows ; draws } ; club } ; real avilés cf } = true
Output: select the row whose draws record of all rows is maximum . the club record of this row is real avilés cf .
and { only { filter_less { all_rows ; prize money ( usd ) ; 25000 } } ; eq { hop { filter_less { all_rows ; prize money ( usd ) ; 25000 } ; name } ; jan kodeå ¡ } } = true
Output: select the rows whose prize money  usd  record is less than 25000 . there is only one such row in the table . the name record of this unqiue row is jan kodeå ¡ .
most_eq { all_rows ; result ; l } = true
Output: for the result records of all rows , most of them fuzzily match to l .
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; september 24 , 1989 } = true
Output: select the row whose attendance record of all rows is 1st maximum . the date record of this row is september 24 , 1989 .
eq { count { filter_less { filter_eq { all_rows ; game site ; kingdome } ; attendance ; 62000 } } ; 3 } = true
Output: select the rows whose game site record fuzzily matches to kingdome . among these rows , select the rows whose attendance record is less than 62000 . the number of such rows is 3 .
most_greater { all_rows ; average score ; 82 } = true
Output: for the average score records of all rows , most of them are greater than 82 .
eq { hop { argmax { all_rows ; extra points 1 point } ; player } ; bruce shorts } = true
Output: select the row whose extra points 1 point record of all rows is maximum . the player record of this row is bruce shorts .
eq { count { filter_eq { all_rows ; away losses ; 0 } } ; 4 } = true
Output: select the rows whose away losses record is equal to 0 . the number of such rows is 4 .
eq { hop { argmax { all_rows ; attendance } ; date } ; may 22 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is may 22 .
round_eq { sum { all_rows ; wins } ; 45 } = true
Output: the sum of the wins record of all rows is 45 .
and { only { filter_eq { all_rows ; decision ; wall } } ; eq { hop { filter_eq { all_rows ; decision ; wall } ; date } ; september 20 } } = true
Output: select the rows whose decision record fuzzily matches to wall . there is only one such row in the table . the date record of this unqiue row is september 20 .
eq { count { filter_eq { filter_eq { all_rows ; winning percentage ; 1 } ; wins ; 2 } } ; 2 } = true
Output: select the rows whose winning percentage record is equal to 1 . among these rows , select the rows whose wins record is equal to 2 . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; title ; pilot } ; no in series } ; hop { filter_eq { all_rows ; title ; nightmare on al 's street } ; no in series } } = true
Output: select the rows whose title record fuzzily matches to pilot . take the no in series record of this row . select the rows whose title record fuzzily matches to nightmare on al s street . take the no in series record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; us viewers ( millions ) } ; 0.944 } = true
Output: the average of the us viewers  millions  record of all rows is 0.944 .
eq { hop { nth_argmax { all_rows ; points ; 2 } ; player } ; andy kaufmann } = true
Output: select the row whose points record of all rows is 2nd maximum . the player record of this row is andy kaufmann .
and { only { filter_eq { all_rows ; years ; 9 - 15 } } ; eq { hop { filter_eq { all_rows ; years ; 9 - 15 } ; name } ; gore high school } } = true
Output: select the rows whose years record fuzzily matches to 9  15 . there is only one such row in the table . the name record of this unqiue row is gore high school .
round_eq { avg { all_rows ; high points } ; 30 } = true
Output: the average of the high points record of all rows is 30 .
greater { hop { filter_eq { all_rows ; home team ; essendon } ; crowd } ; hop { filter_eq { all_rows ; home team ; footscray } ; crowd } } = true
Output: select the rows whose home team record fuzzily matches to essendon . take the crowd record of this row . select the rows whose home team record fuzzily matches to footscray . take the crowd record of this row . the first record is greater than the second record .
only { filter_eq { all_rows ; date ; monday } } = true
Output: select the rows whose date record fuzzily matches to monday . there is only one such row in the table .
eq { count { filter_greater { all_rows ; year founded ; 2000 } } ; 3 } = true
Output: select the rows whose year founded record is greater than 2000 . the number of such rows is 3 .
less { hop { filter_eq { all_rows ; vacator ; john l mcclellan ( d ) } ; reason for change } ; hop { filter_eq { all_rows ; vacator ; lee metcalf ( d ) } ; reason for change } } = true
Output: select the rows whose vacator record fuzzily matches to john l mcclellan  d  . take the reason for change record of this row . select the rows whose vacator record fuzzily matches to lee metcalf  d  . take the reason for change record of this row . the first record is less than the second record .
and { greater { hop { filter_eq { all_rows ; date ; november 17 , 1968 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 24 , 1968 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; november 17 , 1968 } ; attendance } ; 48271 } ; eq { hop { filter_eq { all_rows ; date ; november 24 , 1968 } ; attendance } ; 18305 } } } = true
Output: select the rows whose date record fuzzily matches to november 17 , 1968 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 24 , 1968 . take the attendance record of this row . the first record is greater than the second record . the attendance record of the first row is 48271 . the attendance record of the second row is 18305 .