niryuu/sni-each-converted huggingface dataset:
and { only { filter_eq { all_rows ; venue ; windy hill } } ; and { eq { hop { filter_eq { all_rows ; venue ; windy hill } ; home team } ; essendon } ; eq { hop { filter_eq { all_rows ; venue ; windy hill } ; away team } ; st kilda } } } = true
Output: select the rows whose venue record fuzzily matches to windy hill . there is only one such row in the table . the home team record of this unqiue row is essendon . the away team record of this unqiue row is st kilda .
round_eq { avg { all_rows ; length } ; 55 } = true
Output: the average of the length record of all rows is 55 .
round_eq { avg { all_rows ; us viewers ( millions ) } ; 17.89 } = true
Output: the average of the us viewers  millions  record of all rows is 17.89 .
most_not_eq { all_rows ; ownership ; state - owned } = true
Output: for the ownership records of all rows , most of them do not match to state  owned .
and { only { filter_eq { all_rows ; nationality ; united kingdom canada } } ; eq { hop { filter_eq { all_rows ; nationality ; united kingdom canada } ; player } ; david lee } } = true
Output: select the rows whose nationality record fuzzily matches to united kingdom canada . there is only one such row in the table . the player record of this unqiue row is david lee .
eq { hop { nth_argmax { all_rows ; total ; 1 } ; nation } ; china } = true
Output: select the row whose total record of all rows is 1st maximum . the nation record of this row is china .
and { only { filter_eq { all_rows ; draw ; 16 } } ; eq { hop { filter_eq { all_rows ; draw ; 16 } ; competition } ; league } } = true
Output: select the rows whose draw record is equal to 16 . there is only one such row in the table . the competition record of this unqiue row is league .
round_eq { avg { all_rows ; away team score } ; 14.2 } = true
Output: the average of the away team score record of all rows is 14.2 .
eq { hop { nth_argmin { all_rows ; score ; 1 } ; player } ; labron harris } = true
Output: select the row whose score record of all rows is 1st minimum . the player record of this row is labron harris .
eq { count { filter_eq { all_rows ; successor ; vacant } } ; 4 } = true
Output: select the rows whose successor record fuzzily matches to vacant . the number of such rows is 4 .
round_eq { avg { filter_less { all_rows ; points ; 100 } ; points } ; 71.4 } = true
Output: select the rows whose points record is less than 100 . the average of the points record of these rows is 71.4 .
and { only { filter_greater { all_rows ; points ; 0 } } ; eq { hop { filter_greater { all_rows ; points ; 0 } ; year } ; 1951 } } = true
Output: select the rows whose points record is greater than 0 . there is only one such row in the table . the year record of this unqiue row is 1951 .
round_eq { avg { all_rows ; points } ; 23.25 } = true
Output: the average of the points record of all rows is 23.25 .
eq { hop { argmax { filter_less { all_rows ; average ; 1.0 } ; points } ; team } ; estudiantes de la plata } = true
Output: select the rows whose average record is less than 1.0 . select the row whose points record of these rows is maximum . the team record of this row is estudiantes de la plata .
eq { hop { argmin { all_rows ; date } ; partner } ; melle van gemerden } = true
Output: select the row whose date record of all rows is minimum . the partner record of this row is melle van gemerden .
eq { count { filter_eq { all_rows ; date ; december } } ; 4 } = true
Output: select the rows whose date record fuzzily matches to december . the number of such rows is 4 .
eq { count { filter_all { all_rows ; acc team } } ; 11 } = true
Output: select the rows whose acc team record is arbitrary . the number of such rows is 11 .
round_eq { avg { all_rows ; division record } ; 3.14 } = true
Output: the average of the division record record of all rows is 3.14 .
round_eq { avg { all_rows ; gold } ; 2.86 } = true
Output: the average of the gold record of all rows is 2.86 .
eq { hop { nth_argmax { all_rows ; goal gain ; 3 } ; team } ; south china } = true
Output: select the row whose goal gain record of all rows is 3rd maximum . the team record of this row is south china .
eq { count { filter_eq { all_rows ; nor 1 ; ret } } ; 3 } = true
Output: select the rows whose nor 1 record fuzzily matches to ret . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; writer ( s ) ; steven melching } } ; 3 } = true
Output: select the rows whose writer  s  record fuzzily matches to steven melching . the number of such rows is 3 .
only { filter_eq { all_rows ; role ; guest } } = true
Output: select the rows whose role record fuzzily matches to guest . there is only one such row in the table .
eq { count { filter_eq { all_rows ; score ; 0 } } ; 7 } = true
Output: select the rows whose score record is equal to 0 . the number of such rows is 7 .
eq { hop { argmin { all_rows ; date } ; opponent } ; japan } = true
Output: select the row whose date record of all rows is minimum . the opponent record of this row is japan .
eq { hop { nth_argmin { all_rows ; overall pick ; 3 } ; player } ; dennis byrd } = true
Output: select the row whose overall pick record of all rows is 3rd minimum . the player record of this row is dennis byrd .
less { hop { filter_eq { all_rows ; tag team ; rosey and the hurricane } ; eliminated } ; hop { filter_eq { all_rows ; tag team ; jindrak and cade } ; eliminated } } = true
Output: select the rows whose tag team record fuzzily matches to rosey and the hurricane . take the eliminated record of this row . select the rows whose tag team record fuzzily matches to jindrak and cade . take the eliminated record of this row . the first record is less than the second record .
round_eq { sum { all_rows ; location attendance } ; 240,206 } = true
Output: the sum of the location attendance record of all rows is 240,206 .
and { only { filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 } } ; eq { hop { filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 } ; year } ; 2012 } } = true
Output: select the rows whose summary record fuzzily matches to runners  up . among these rows , select the rows whose losses record is equal to 11 . there is only one such row in the table . the year record of this unqiue row is 2012 .
greater { hop { filter_eq { all_rows ; date ; september 8 , 1986 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 21 , 1986 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to september 8 , 1986 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 21 , 1986 . take the attendance record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; date ; 16 june 1951 } } ; 6 } = true
Output: select the rows whose date record fuzzily matches to 16 june 1951 . the number of such rows is 6 .
eq { hop { nth_argmin { all_rows ; year ; 1 } ; model } ; type 26 } = true
Output: select the row whose year record of all rows is 1st minimum . the model record of this row is type 26 .
most_eq { all_rows ; injured ; 0 } = true
Output: for the injured records of all rows , most of them are equal to 0 .
and { only { filter_eq { all_rows ; implementation ; dedicated hardware } } ; eq { hop { filter_eq { all_rows ; implementation ; dedicated hardware } ; vendor and type } ; enterasys switches } } = true
Output: select the rows whose implementation record fuzzily matches to dedicated hardware . there is only one such row in the table . the vendor and type record of this unqiue row is enterasys switches .
eq { hop { argmax { all_rows ; score } ; score } ; 3 - 2 } = true
Output: select the row whose score record of all rows is maximum . the score record of this row is 3  2 .
greater { hop { filter_eq { all_rows ; station ; tsu } ; distance ( km ) } ; hop { filter_eq { all_rows ; station ; kawage } ; distance ( km ) } } = true
Output: select the rows whose station record fuzzily matches to tsu . take the distance  km  record of this row . select the rows whose station record fuzzily matches to kawage . take the distance  km  record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; driver ; kenny bräck } ; time / retired } ; hop { filter_eq { all_rows ; driver ; jimmy vasser } ; time / retired } } = true
Output: select the rows whose driver record fuzzily matches to kenny bräck . take the time  retired record of this row . select the rows whose driver record fuzzily matches to jimmy vasser . take the time  retired record of this row . the first record is less than the second record .
round_eq { sum { all_rows ; championships } ; 2 } = true
Output: the sum of the championships record of all rows is 2 .
greater { hop { filter_eq { all_rows ; venue ; punt road oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; glenferrie oval } ; crowd } } = true
Output: select the rows whose venue record fuzzily matches to punt road oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to glenferrie oval . take the crowd record of this row . the first record is greater than the second record .
most_eq { all_rows ; tournament ; 25000 } = true
Output: for the tournament records of all rows , most of them fuzzily match to 25000 .
eq { max { filter_eq { all_rows ; score ; w } ; score } ; w 107 - 92 ( ot ) } = true
Output: select the rows whose score record fuzzily matches to w . the maximum score record of these rows is w 107  92  ot  .
eq { count { filter_eq { all_rows ; directed by ; steve buscemi } } ; 2 } = true
Output: select the rows whose directed by record fuzzily matches to steve buscemi . the number of such rows is 2 .
eq { hop { argmax { all_rows ; kilometer } ; number } ; 31 } = true
Output: select the row whose kilometer record of all rows is maximum . the number record of this row is 31 .
most_eq { filter_eq { all_rows ; date ; june } ; score ; 2-0 } = true
Output: select the rows whose date record fuzzily matches to june . for the score records of these rows , most of them fuzzily match to 20 .
all_eq { all_rows ; date ; 30 may 1964 } = true
Output: for the date records of all rows , all of them fuzzily match to 30 may 1964 .
round_eq { sum { filter_eq { all_rows ; competition ; 1979 president 's cup } ; score } ; 4 } = true
Output: select the rows whose competition record fuzzily matches to 1979 president s cup . the sum of the score record of these rows is 4 .
eq { hop { nth_argmin { all_rows ; time ; 1 } ; race } ; maiden } = true
Output: select the row whose time record of all rows is 1st minimum . the race record of this row is maiden .
greater { hop { filter_eq { all_rows ; nation ; cuba } ; total } ; hop { filter_eq { all_rows ; nation ; jamaica } ; total } } = true
Output: select the rows whose nation record fuzzily matches to cuba . take the total record of this row . select the rows whose nation record fuzzily matches to jamaica . take the total record of this row . the first record is greater than the second record .
eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; moulins } = true
Output: select the row whose date record of all rows is 2nd minimum . the tournament record of this row is moulins .
only { filter_eq { all_rows ; runner - up ; skonto fc } } = true
Output: select the rows whose runner  up record fuzzily matches to skonto fc . there is only one such row in the table .
and { only { filter_eq { all_rows ; aggregate score ; 0 } } ; eq { hop { filter_eq { all_rows ; aggregate score ; 0 } ; opposition } ; fc köln } } = true
Output: select the rows whose aggregate score record fuzzily matches to 0 . there is only one such row in the table . the opposition record of this unqiue row is fc köln .
eq { count { filter_eq { all_rows ; total ( kg ) ; 300.0 } } ; 2 } = true
Output: select the rows whose total  kg  record is equal to 300.0 . the number of such rows is 2 .
most_greater_eq { all_rows ; memory bandwidth ( gb / s ) ; 17.1 } = true
Output: for the memory bandwidth  gb  s  records of all rows , most of them are greater than or equal to 17.1 .
round_eq { avg { all_rows ; total freshwater withdrawal } ; 60.34 } = true
Output: the average of the total freshwater withdrawal record of all rows is 60.34 .
eq { hop { nth_argmin { all_rows ; original air date ; 5 } ; series } ; 5 } = true
Output: select the row whose original air date record of all rows is 5th minimum . the series record of this row is 5 .
eq { hop { nth_argmax { all_rows ; round ; 3 } ; player } ; tyree davis } = true
Output: select the row whose round record of all rows is 3rd maximum . the player record of this row is tyree davis .
eq { max { filter_eq { all_rows ; venue ; park } ; away team score } ; 21.10 ( 136 ) } = true
Output: select the rows whose venue record fuzzily matches to park . the maximum away team score record of these rows is 21.10  136  .
less { hop { filter_eq { all_rows ; name ; andrew } ; date } ; hop { filter_eq { all_rows ; name ; amanda } ; date } } = true
Output: select the rows whose name record fuzzily matches to andrew . take the date record of this row . select the rows whose name record fuzzily matches to amanda . take the date record of this row . the first record is less than the second record .
eq { hop { nth_argmin { filter_eq { all_rows ; date ; october } ; date ; 1 } ; opponent } ; pittsburgh steelers } = true
Output: select the rows whose date record fuzzily matches to october . select the row whose date record of these rows is 1st minimum . the opponent record of this row is pittsburgh steelers .
most_less_eq { all_rows ; original air date ; february 2005 } = true
Output: for the original air date records of all rows , most of them are less than or equal to february 2005 .
eq { count { filter_eq { filter_less { all_rows ; year ; 1960 } ; laps ; 200 } } ; 5 } = true
Output: select the rows whose year record is less than 1960 . among these rows , select the rows whose laps record is equal to 200 . the number of such rows is 5 .
and { only { filter_eq { all_rows ; tournament ; fukuoka , japan } } ; eq { hop { filter_eq { all_rows ; tournament ; fukuoka , japan } ; date } ; may 11 , 2003 } } = true
Output: select the rows whose tournament record fuzzily matches to fukuoka , japan . there is only one such row in the table . the date record of this unqiue row is may 11 , 2003 .
greater { hop { filter_eq { all_rows ; player ; fred couples } ; weeks } ; hop { filter_eq { all_rows ; player ; david duval } ; weeks } } = true
Output: select the rows whose player record fuzzily matches to fred couples . take the weeks record of this row . select the rows whose player record fuzzily matches to david duval . take the weeks record of this row . the first record is greater than the second record .
and { greater { hop { filter_eq { all_rows ; game ; 69 } ; score } ; hop { filter_eq { all_rows ; game ; 66 } ; score } } ; and { eq { hop { filter_eq { all_rows ; game ; 69 } ; opponent } ; boston bruins } ; eq { hop { filter_eq { all_rows ; game ; 66 } ; opponent } ; boston bruins } } } = true
Output: select the rows whose game record fuzzily matches to 69 . take the score record of this row . select the rows whose game record fuzzily matches to 66 . take the score record of this row . the first record is greater than the second record . the opponent record of the first row is boston bruins . the opponent record of the second row is boston bruins .
round_eq { avg { all_rows ; points } ; 51.8 } = true
Output: the average of the points record of all rows is 51.8 .
eq { hop { argmin { filter_eq { all_rows ; competition ; european zone } ; year } ; score } ; 1 - 4 } = true
Output: select the rows whose competition record fuzzily matches to european zone . select the row whose year record of these rows is minimum . the score record of this row is 1  4 .
eq { count { filter_greater { all_rows ; enrollment ; 2000 } } ; 5 } = true
Output: select the rows whose enrollment record is greater than 2000 . the number of such rows is 5 .
round_eq { avg { all_rows ; duration } ; 6 months 7 days } = true
Output: the average of the duration record of all rows is 6 months 7 days .
less { hop { filter_eq { all_rows ; tournament ; bordeaux , france } ; date } ; hop { filter_eq { all_rows ; tournament ; san marino } ; date } } = true
Output: select the rows whose tournament record fuzzily matches to bordeaux , france . take the date record of this row . select the rows whose tournament record fuzzily matches to san marino . take the date record of this row . the first record is less than the second record .
less { hop { filter_eq { all_rows ; name ; margravine johanna of baden - baden } ; became duchess } ; hop { filter_eq { all_rows ; name ; victoria of saxe - coburg and gotha } ; became duchess } } = true
Output: select the rows whose name record fuzzily matches to margravine johanna of baden  baden . take the became duchess record of this row . select the rows whose name record fuzzily matches to victoria of saxe  coburg and gotha . take the became duchess record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; pick ; 3 } } ; eq { hop { filter_eq { all_rows ; pick ; 3 } ; player } ; dennis ververgaert } } = true
Output: select the rows whose pick record is equal to 3 . there is only one such row in the table . the player record of this unqiue row is dennis ververgaert .
eq { hop { nth_argmax { all_rows ; total ; 3 } ; nation } ; japan ( jpn ) } = true
Output: select the row whose total record of all rows is 3rd maximum . the nation record of this row is japan  jpn  .
eq { count { filter_eq { all_rows ; status ; village } } ; 4 } = true
Output: select the rows whose status record fuzzily matches to village . the number of such rows is 4 .
eq { hop { argmax { all_rows ; matches } ; goalkeeper } ; asier riesgo } = true
Output: select the row whose matches record of all rows is maximum . the goalkeeper record of this row is asier riesgo .
and { only { filter_eq { all_rows ; nationality ; united states } } ; eq { hop { filter_eq { all_rows ; nationality ; united states } ; player } ; matthew anderson } } = true
Output: select the rows whose nationality record fuzzily matches to united states . there is only one such row in the table . the player record of this unqiue row is matthew anderson .
and { only { filter_eq { all_rows ; map colour ; black } } ; eq { hop { filter_eq { all_rows ; map colour ; black } ; name } ; northern line } } = true
Output: select the rows whose map colour record fuzzily matches to black . there is only one such row in the table . the name record of this unqiue row is northern line .
eq { count { filter_greater { all_rows ; runs ; 300 } } ; 7 } = true
Output: select the rows whose runs record is greater than 300 . the number of such rows is 7 .
eq { count { filter_eq { filter_greater { all_rows ; attendance ; 25000 } ; date ; october } } ; 3 } = true
Output: select the rows whose attendance record is greater than 25000 . among these rows , select the rows whose date record fuzzily matches to october . the number of such rows is 3 .
eq { count { filter_greater_eq { all_rows ; episodes ; 8 } } ; 3 } = true
Output: select the rows whose episodes record is greater than or equal to 8 . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; matches ; 2 } ; name } ; glenn mcgrath } = true
Output: select the row whose matches record of all rows is 2nd maximum . the name record of this row is glenn mcgrath .
eq { count { filter_eq { all_rows ; location ; wabash } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to wabash . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; goal difference ; 3 } ; club } ; real oviedo } = true
Output: select the row whose goal difference record of all rows is 3rd maximum . the club record of this row is real oviedo .
round_eq { avg { all_rows ; crowd } ; 24248 } = true
Output: the average of the crowd record of all rows is 24248 .
eq { min { filter_eq { all_rows ; competition ; friendly match } ; date } ; 7 november 1997 } = true
Output: select the rows whose competition record fuzzily matches to friendly match . the minimum date record of these rows is 7 november 1997 .
eq { count { filter_eq { filter_eq { all_rows ; location ; scotiabank place } ; score ; w } } ; 4 } = true
Output: select the rows whose location record fuzzily matches to scotiabank place . among these rows , select the rows whose score record fuzzily matches to w . the number of such rows is 4 .
round_eq { avg { filter_eq { all_rows ; result ; wi } ; result } ; 8 } = true
Output: select the rows whose result record fuzzily matches to wi . the average of the result record of these rows is 8 .
eq { count { filter_all { all_rows ; regional county municipality ( rcm ) } } ; 6 } = true
Output: select the rows whose regional county municipality  rcm  record is arbitrary . the number of such rows is 6 .
round_eq { sum { all_rows ; total } ; 48 } = true
Output: the sum of the total record of all rows is 48 .
eq { hop { argmin { all_rows ; weight ( kg ) } ; race } ; cox plate } = true
Output: select the row whose weight  kg  record of all rows is minimum . the race record of this row is cox plate .
round_eq { avg { all_rows ; year joined } ; 1977 } = true
Output: the average of the year joined record of all rows is 1977 .
less { hop { filter_eq { all_rows ; loco name ; earl of mount edgcumbe } ; build date } ; hop { filter_eq { all_rows ; loco name ; pyramus } ; build date } } = true
Output: select the rows whose loco name record fuzzily matches to earl of mount edgcumbe . take the build date record of this row . select the rows whose loco name record fuzzily matches to pyramus . take the build date record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; laps } ; year } ; 1989 } = true
Output: select the row whose laps record of all rows is maximum . the year record of this row is 1989 .
eq { hop { nth_argmin { all_rows ; draw ; 1 } ; artist } ; alma čardžić } = true
Output: select the row whose draw record of all rows is 1st minimum . the artist record of this row is alma čardžić .
eq { count { filter_eq { all_rows ; manufacturer ; suzuki } } ; 2 } = true
Output: select the rows whose manufacturer record fuzzily matches to suzuki . the number of such rows is 2 .
and { only { filter_eq { all_rows ; college ; wake forest } } ; eq { hop { filter_eq { all_rows ; college ; wake forest } ; player } ; mark moroz } } = true
Output: select the rows whose college record fuzzily matches to wake forest . there is only one such row in the table . the player record of this unqiue row is mark moroz .
and { only { filter_greater { all_rows ; 2004 ; 1000 } } ; eq { hop { filter_greater { all_rows ; 2004 ; 1000 } ; tournament } ; year end ranking } } = true
Output: select the rows whose 2004 record is greater than 1000 . there is only one such row in the table . the tournament record of this unqiue row is year end ranking .
all_eq { all_rows ; opponent ; new york islanders } = true
Output: for the opponent records of all rows , all of them fuzzily match to new york islanders .
eq { hop { argmin { all_rows ; founded } ; institution } ; college of charleston } = true
Output: select the row whose founded record of all rows is minimum . the institution record of this row is college of charleston .
eq { hop { nth_argmax { all_rows ; negotiable debt at mid - 2005 ( us dollar bn equivalent ) ; 2 } ; currency } ; us dollar } = true
Output: select the row whose negotiable debt at mid  2005  us dollar bn equivalent  record of all rows is 2nd maximum . the currency record of this row is us dollar .
all_eq { all_rows ; width ; 96 } = true
Output: for the width records of all rows , all of them are equal to 96 .
round_eq { avg { all_rows ; sequence similarity } ; 70 } = true
Output: the average of the sequence similarity record of all rows is 70 .
eq { hop { argmin { all_rows ; laps } ; rider } ; carlos checa } = true
Output: select the row whose laps record of all rows is minimum . the rider record of this row is carlos checa .
eq { count { filter_greater { filter_greater { all_rows ; home team score ; 9.0 } ; crowd ; 10000 } } ; 2 } = true
Output: select the rows whose home team score record is greater than 9.0 . among these rows , select the rows whose crowd record is greater than 10000 . the number of such rows is 2 .
most_eq { all_rows ; seat percentage ; 0.0 } = true
Output: for the seat percentage records of all rows , most of them are equal to 0.0 .
eq { hop { argmax { all_rows ; total offense } ; player } ; devin gardner } = true
Output: select the row whose total offense record of all rows is maximum . the player record of this row is devin gardner .
eq { count { filter_eq { all_rows ; year ; 1975 } } ; 3 } = true
Output: select the rows whose year record is equal to 1975 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; joined ; 2007 } } ; 2 } = true
Output: select the rows whose joined record is equal to 2007 . the number of such rows is 2 .
eq { hop { nth_argmin { all_rows ; time ; 2 } ; athlete } ; peter hardcastle } = true
Output: select the row whose time record of all rows is 2nd minimum . the athlete record of this row is peter hardcastle .
eq { count { filter_less { filter_greater { all_rows ; altitude ( mslm ) ; 100 } ; population ; 10000 } } ; 2 } = true
Output: select the rows whose altitude  mslm  record is greater than 100 . among these rows , select the rows whose population record is less than 10000 . the number of such rows is 2 .
round_eq { avg { all_rows ; vertical } ; 720 } = true
Output: the average of the vertical record of all rows is 720 .
eq { count { filter_eq { all_rows ; writer / composer ; ken walther } } ; 2 } = true
Output: select the rows whose writer  composer record fuzzily matches to ken walther . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; result ; w } } ; 5 } = true
Output: select the rows whose result record fuzzily matches to w . the number of such rows is 5 .
most_eq { all_rows ; winning amount ; rs 10 , 00000 } = true
Output: for the winning amount records of all rows , most of them fuzzily match to rs 10 , 00000 .
eq { count { filter_eq { all_rows ; industry ; oil and gas } } ; 4 } = true
Output: select the rows whose industry record fuzzily matches to oil and gas . the number of such rows is 4 .
eq { hop { nth_argmax { all_rows ; uncut run time ; 2 } ; title } ; addio zio tom } = true
Output: select the row whose uncut run time record of all rows is 2nd maximum . the title record of this row is addio zio tom .
round_eq { avg { all_rows ; attendance } ; 36859 } = true
Output: the average of the attendance record of all rows is 36859 .
less { hop { filter_eq { all_rows ; player ; jon koncak } ; years in orlando } ; hop { filter_eq { all_rows ; player ; tim kempton } ; years in orlando } } = true
Output: select the rows whose player record fuzzily matches to jon koncak . take the years in orlando record of this row . select the rows whose player record fuzzily matches to tim kempton . take the years in orlando record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; nationality ; portuguese } } ; eq { hop { filter_eq { all_rows ; nationality ; portuguese } ; elector } ; joão pedro julião } } = true
Output: select the rows whose nationality record fuzzily matches to portuguese . there is only one such row in the table . the elector record of this unqiue row is joão pedro julião .
less { hop { filter_eq { all_rows ; club ; fk valmiera } ; position } ; hop { filter_eq { all_rows ; club ; fk auda kekava } ; position } } = true
Output: select the rows whose club record fuzzily matches to fk valmiera . take the position record of this row . select the rows whose club record fuzzily matches to fk auda kekava . take the position record of this row . the first record is less than the second record .
most_eq { all_rows ; party ; democrat } = true
Output: for the party records of all rows , most of them fuzzily match to democrat .
eq { hop { nth_argmin { all_rows ; date ; 2 } ; venue } ; port of spain , trinidad and tobago } = true
Output: select the row whose date record of all rows is 2nd minimum . the venue record of this row is port of spain , trinidad and tobago .
eq { count { filter_eq { filter_eq { all_rows ; result ; re-elected } ; first elected ; 193 } } ; 3 } = true
Output: select the rows whose result record fuzzily matches to reelected . among these rows , select the rows whose first elected record fuzzily matches to 193 . the number of such rows is 3 .
and { only { filter_eq { all_rows ; matches ; 410 } } ; eq { hop { filter_eq { all_rows ; matches ; 410 } ; name } ; jimmy kelly } } = true
Output: select the rows whose matches record is equal to 410 . there is only one such row in the table . the name record of this unqiue row is jimmy kelly .
eq { count { filter_eq { all_rows ; round of 16 ; did not advance } } ; 2 } = true
Output: select the rows whose round of 16 record fuzzily matches to did not advance . the number of such rows is 2 .
round_eq { sum { filter_eq { all_rows ; year ; 1985 } ; points } ; 8 } = true
Output: select the rows whose year record is equal to 1985 . the sum of the points record of these rows is 8 .
round_eq { sum { filter_eq { all_rows ; location attendance ; ford center } ; location attendance } ; over 36000 } = true
Output: select the rows whose location attendance record fuzzily matches to ford center . the sum of the location attendance record of these rows is over 36000 .
and { only { filter_eq { all_rows ; manager ; tommy shields } } ; eq { hop { filter_eq { all_rows ; manager ; tommy shields } ; finish } ; 4th } } = true
Output: select the rows whose manager record fuzzily matches to tommy shields . there is only one such row in the table . the finish record of this unqiue row is 4th .
most_eq { all_rows ; intra - molecular structure ; no } = true
Output: for the intra  molecular structure records of all rows , most of them fuzzily match to no .
eq { hop { argmax { all_rows ; year } ; driver } ; toni teittinen } = true
Output: select the row whose year record of all rows is maximum . the driver record of this row is toni teittinen .
eq { hop { nth_argmax { filter_eq { all_rows ; position ; fw } ; number ; 2 } ; player } ; fwayo tembo } = true
Output: select the rows whose position record fuzzily matches to fw . select the row whose number record of these rows is 2nd maximum . the player record of this row is fwayo tembo .
eq { count { filter_eq { all_rows ; 2nd member ; dugald mccoig cowan } } ; 4 } = true
Output: select the rows whose 2nd member record fuzzily matches to dugald mccoig cowan . the number of such rows is 4 .
round_eq { sum { all_rows ; total produced } ; 229 } = true
Output: the sum of the total produced record of all rows is 229 .
only { filter_eq { all_rows ; 7:30 ; jeopardy! } } = true
Output: select the rows whose 7:30 record fuzzily matches to jeopardy! . there is only one such row in the table .
most_greater { all_rows ; crowd ; 17000 } = true
Output: for the crowd records of all rows , most of them are greater than 17000 .
and { only { filter_eq { all_rows ; position ; c } } ; eq { hop { filter_eq { all_rows ; position ; c } ; player } ; jeff winchester } } = true
Output: select the rows whose position record fuzzily matches to c . there is only one such row in the table . the player record of this unqiue row is jeff winchester .
and { only { filter_eq { all_rows ; location ; south africa } } ; eq { hop { filter_eq { all_rows ; location ; south africa } ; name } ; likhoelesaurus } } = true
Output: select the rows whose location record fuzzily matches to south africa . there is only one such row in the table . the name record of this unqiue row is likhoelesaurus .
most_greater { all_rows ; crowd ; 10,000 } = true
Output: for the crowd records of all rows , most of them are greater than 10,000 .
eq { count { filter_eq { filter_eq { all_rows ; manufacturer ; beyer , peacock & co } ; manufacturer ; beyer , peacock & co } } ; 3 } = true
Output: select the rows whose manufacturer record fuzzily matches to beyer , peacock  co . among these rows , select the rows whose manufacturer record fuzzily matches to beyer , peacock  co . the number of such rows is 3 .
eq { count { filter_less { all_rows ; attendance ; 40000 } } ; 3 } = true
Output: select the rows whose attendance record is less than 40000 . the number of such rows is 3 .
and { only { filter_not_eq { all_rows ; deleted ; - } } ; eq { hop { filter_not_eq { all_rows ; deleted ; - } ; name } ; mowbray engineering } } = true
Output: select the rows whose deleted record is not equal to  . there is only one such row in the table . the name record of this unqiue row is mowbray engineering .
less { hop { filter_eq { all_rows ; months in malayalam era ; chingam } ; gregorian calendar } ; hop { filter_eq { all_rows ; months in malayalam era ; tulam } ; gregorian calendar } } = true
Output: select the rows whose months in malayalam era record fuzzily matches to chingam . take the gregorian calendar record of this row . select the rows whose months in malayalam era record fuzzily matches to tulam . take the gregorian calendar record of this row . the first record is less than the second record .
eq { hop { argmin { filter_greater { all_rows ; acquired ; 2001 } ; capacity , mln tpa } ; name } ; trn } = true
Output: select the rows whose acquired record is greater than 2001 . select the row whose capacity , mln tpa record of these rows is minimum . the name record of this row is trn .
eq { count { filter_less { all_rows ; year ; 2010 } } ; 3 } = true
Output: select the rows whose year record is less than 2010 . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; capacity ; 2 } ; team } ; goyang hi fc } = true
Output: select the row whose capacity record of all rows is 2nd maximum . the team record of this row is goyang hi fc .
eq { count { filter_eq { all_rows ; winning driver ; jackie stewart } } ; 2 } = true
Output: select the rows whose winning driver record fuzzily matches to jackie stewart . the number of such rows is 2 .
round_eq { avg { all_rows ; tournaments } ; 11.46 } = true
Output: the average of the tournaments record of all rows is 11.46 .
most_greater_eq { all_rows ; weight ; 200 } = true
Output: for the weight records of all rows , most of them are greater than or equal to 200 .
eq { count { filter_less { filter_less { all_rows ; years as tallest ; 1902 } ; height ft ( m ) ; 200 } } ; 2 } = true
Output: select the rows whose years as tallest record is less than 1902 . among these rows , select the rows whose height ft  m  record is less than 200 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; score } ; opponent team } ; summerville fc } = true
Output: select the row whose score record of all rows is maximum . the opponent team record of this row is summerville fc .
round_eq { avg { all_rows ; enrolment } ; 831 } = true
Output: the average of the enrolment record of all rows is 831 .
round_eq { sum { all_rows ; points } ; 1,357 } = true
Output: the sum of the points record of all rows is 1,357 .
eq { hop { nth_argmin { filter_eq { all_rows ; coach ; brian noble } ; main article ; 4 } ; lost } ; 7 } = true
Output: select the rows whose coach record fuzzily matches to brian noble . select the row whose main article record of these rows is 4th minimum . the lost record of this row is 7 .
round_eq { sum { all_rows ; laps } ; 2021 } = true
Output: the sum of the laps record of all rows is 2021 .
most_greater { all_rows ; apparent magnitude ; 10.0 } = true
Output: for the apparent magnitude records of all rows , most of them are greater than 10.0 .
and { less { hop { filter_eq { all_rows ; winner ; ryan sutter } ; premiered } ; hop { filter_eq { all_rows ; winner ; jesse csincsak } ; premiered } } ; and { eq { hop { filter_eq { all_rows ; winner ; ryan sutter } ; premiered } ; january 8 , 2003 } ; eq { hop { filter_eq { all_rows ; winner ; jesse csincsak } ; premiered } ; may 19 , 2008 } } } = true
Output: select the rows whose winner record fuzzily matches to ryan sutter . take the premiered record of this row . select the rows whose winner record fuzzily matches to jesse csincsak . take the premiered record of this row . the first record is less than the second record . the premiered record of the first row is january 8 , 2003 . the premiered record of the second row is may 19 , 2008 .
greater { hop { filter_eq { all_rows ; nation ; south korea } ; bronze } ; hop { filter_eq { all_rows ; nation ; estonia } ; bronze } } = true
Output: select the rows whose nation record fuzzily matches to south korea . take the bronze record of this row . select the rows whose nation record fuzzily matches to estonia . take the bronze record of this row . the first record is greater than the second record .
eq { count { filter_greater_eq { all_rows ; total ; 10 } } ; 3 } = true
Output: select the rows whose total record is greater than or equal to 10 . the number of such rows is 3 .
and { eq { nth_min { all_rows ; original airdate ; 1 } ; january 5 , 2000 } ; eq { hop { nth_argmin { all_rows ; original airdate ; 1 } ; iron chef } ; chen kenichi } } = true
Output: the 1st minimum original airdate record of all rows is january 5 , 2000 . the iron chef record of the row with 1st minimum original airdate record is chen kenichi .
most_less { all_rows ; time ; 21.0 } = true
Output: for the time records of all rows , most of them are less than 21.0 .
eq { count { filter_eq { all_rows ; result ; lost } } ; 4 } = true
Output: select the rows whose result record fuzzily matches to lost . the number of such rows is 4 .
and { eq { hop { filter_eq { all_rows ; headphone model ; ps500 } ; headphone class } ; hop { filter_eq { all_rows ; headphone model ; ps1000 } ; headphone class } } ; and { eq { hop { filter_eq { all_rows ; headphone model ; ps500 } ; headphone class } ; professional } ; eq { hop { filter_eq { all_rows ; headphone model ; ps1000 } ; headphone class } ; professional } } } = true
Output: select the rows whose headphone model record fuzzily matches to ps500 . take the headphone class record of this row . select the rows whose headphone model record fuzzily matches to ps1000 . take the headphone class record of this row . the first record fuzzily matches to the second record . the headphone class record of the first row is professional . the headphone class record of the second row is professional .
eq { count { filter_eq { all_rows ; rank ; 6 } } ; 2 } = true
Output: select the rows whose rank record is equal to 6 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; venue ; neman } ; capacity } ; hop { filter_eq { all_rows ; venue ; darida } ; capacity } } = true
Output: select the rows whose venue record fuzzily matches to neman . take the capacity record of this row . select the rows whose venue record fuzzily matches to darida . take the capacity record of this row . the first record is greater than the second record .
and { only { filter_greater { all_rows ; hk viewers ; 2.20 million } } ; eq { hop { filter_greater { all_rows ; hk viewers ; 2.20 million } ; english title } ; beyond the realm of conscience } } = true
Output: select the rows whose hk viewers record is greater than 2.20 million . there is only one such row in the table . the english title record of this unqiue row is beyond the realm of conscience .
and { only { filter_eq { all_rows ; team ; velocette } } ; eq { hop { filter_eq { all_rows ; team ; velocette } ; rider } ; brian finch } } = true
Output: select the rows whose team record fuzzily matches to velocette . there is only one such row in the table . the rider record of this unqiue row is brian finch .
and { only { filter_eq { all_rows ; wins ; 1 } } ; eq { hop { filter_eq { all_rows ; wins ; 1 } ; season } ; 2010 } } = true
Output: select the rows whose wins record is equal to 1 . there is only one such row in the table . the season record of this unqiue row is 2010 .
eq { hop { argmin { all_rows ; race } ; season } ; 2011 } = true
Output: select the row whose race record of all rows is minimum . the season record of this row is 2011 .
round_eq { avg { all_rows ; rank } ; 20 } = true
Output: the average of the rank record of all rows is 20 .
eq { hop { argmax { all_rows ; date } ; venue } ; halchowk stadium , kathmandu } = true
Output: select the row whose date record of all rows is maximum . the venue record of this row is halchowk stadium , kathmandu .
and { only { filter_eq { all_rows ; score ; 8 } } ; eq { hop { filter_eq { all_rows ; score ; 8 } ; team 1 } ; kashima antlers } } = true
Output: select the rows whose score record fuzzily matches to 8 . there is only one such row in the table . the team 1 record of this unqiue row is kashima antlers .
eq { count { filter_eq { all_rows ; vocalist ; grin } } ; 3 } = true
Output: select the rows whose vocalist record fuzzily matches to grin . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; operating systems ; linux } } ; 3 } = true
Output: select the rows whose operating systems record fuzzily matches to linux . the number of such rows is 3 .
round_eq { avg { all_rows ; goals } ; 82.71 } = true
Output: the average of the goals record of all rows is 82.71 .
round_eq { sum { all_rows ; viewers ( millions ) } ; 54.22 million } = true
Output: the sum of the viewers  millions  record of all rows is 54.22 million .
and { only { filter_eq { all_rows ; acronym ; polimas } } ; eq { hop { filter_eq { all_rows ; acronym ; polimas } ; name in english } ; sultan abdul halim mu'adzam shah } } = true
Output: select the rows whose acronym record fuzzily matches to polimas . there is only one such row in the table . the name in english record of this unqiue row is sultan abdul halim muadzam shah .
and { only { filter_eq { all_rows ; position ; guard } } ; eq { hop { filter_eq { all_rows ; position ; guard } ; name } ; justin anderson } } = true
Output: select the rows whose position record fuzzily matches to guard . there is only one such row in the table . the name record of this unqiue row is justin anderson .
eq { hop { argmin { all_rows ; eliminated } ; wrestler } ; jbl } = true
Output: select the row whose eliminated record of all rows is minimum . the wrestler record of this row is jbl .
and { only { filter_eq { all_rows ; party ; democratic } } ; eq { hop { filter_eq { all_rows ; party ; democratic } ; incumbent } ; clarence f lea } } = true
Output: select the rows whose party record fuzzily matches to democratic . there is only one such row in the table . the incumbent record of this unqiue row is clarence f lea .
eq { hop { argmax { all_rows ; total } ; nation } ; east germany ( gdr ) } = true
Output: select the row whose total record of all rows is maximum . the nation record of this row is east germany  gdr  .
eq { hop { argmax { all_rows ; money requested } ; entrepreneur ( s ) } ; emmie matthews & ed stevens } = true
Output: select the row whose money requested record of all rows is maximum . the entrepreneur  s  record of this row is emmie matthews  ed stevens .
most_eq { all_rows ; socialist ticket ; man } = true
Output: for the socialist ticket records of all rows , most of them fuzzily match to man .
eq { count { filter_eq { all_rows ; date ; 2000 } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to 2000 . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; votes ; 2 } ; party } ; labour } = true
Output: select the row whose votes record of all rows is 2nd maximum . the party record of this row is labour .
eq { count { filter_eq { all_rows ; theme ; n / a } } ; 2 } = true
Output: select the rows whose theme record fuzzily matches to n  a . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; total goals ; 2 } ; total appearances } ; 371 } = true
Output: select the row whose total goals record of all rows is 2nd maximum . the total appearances record of this row is 371 .
most_not_eq { all_rows ; score ; walkover } = true
Output: for the score records of all rows , most of them do not match to walkover .
round_eq { sum { all_rows ; score } ; 4577 } = true
Output: the sum of the score record of all rows is 4577 .
most_greater { all_rows ; points ; 100 } = true
Output: for the points records of all rows , most of them are greater than 100 .
eq { nth_min { filter_eq { all_rows ; competition ; world championships } ; year ; 2 } ; 2001 } = true
Output: select the rows whose competition record fuzzily matches to world championships . the 2nd minimum year record of these rows is 2001 .
eq { hop { nth_argmin { all_rows ; founded ; 1 } ; institution } ; midway college 1 } = true
Output: select the row whose founded record of all rows is 1st minimum . the institution record of this row is midway college 1 .
most_greater { filter_eq { all_rows ; country ; united states } ; score ; 142 } = true
Output: select the rows whose country record fuzzily matches to united states . for the score records of these rows , most of them are greater than 142 .
most_greater { all_rows ; frequency ; 1.5 ghz } = true
Output: for the frequency records of all rows , most of them are greater than 1.5 ghz .
eq { hop { nth_argmax { all_rows ; agg score ; 7 } ; team 1 } ; ilisiakos } = true
Output: select the row whose agg score record of all rows is 7th maximum . the team 1 record of this row is ilisiakos .
eq { count { filter_eq { filter_less { all_rows ; score ; 211 } ; to par ; -7 } } ; 2 } = true
Output: select the rows whose score record is less than 211 . among these rows , select the rows whose to par record is equal to 7 . the number of such rows is 2 .
and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } } ; and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; fcc } ; eq { hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } ; fcc } } } = true
Output: select the rows whose call sign record fuzzily matches to w207af . take the fcc info record of this row . select the rows whose call sign record fuzzily matches to w211ae . take the fcc info record of this row . the first record fuzzily matches to the second record . the fcc info record of the first row is fcc . the fcc info record of the second row is fcc .
and { only { filter_eq { all_rows ; matches ; 6 } } ; eq { hop { filter_eq { all_rows ; matches ; 6 } ; player } ; mark turner } } = true
Output: select the rows whose matches record is equal to 6 . there is only one such row in the table . the player record of this unqiue row is mark turner .
eq { diff { hop { filter_eq { all_rows ; nation ; new zealand } ; bronze } ; hop { filter_eq { all_rows ; nation ; jamaica } ; bronze } } ; 2 } = true
Output: select the rows whose nation record fuzzily matches to new zealand . take the bronze record of this row . select the rows whose nation record fuzzily matches to jamaica . take the bronze record of this row . the first record is 2 larger than the second record .
eq { count { filter_eq { all_rows ; championship ; us open } } ; 6 } = true
Output: select the rows whose championship record fuzzily matches to us open . the number of such rows is 6 .
round_eq { avg { all_rows ; attendance } ; 15720 } = true
Output: the average of the attendance record of all rows is 15720 .
and { only { filter_eq { all_rows ; competition ; uefa euro 2004 qualifying } } ; eq { hop { filter_eq { all_rows ; competition ; uefa euro 2004 qualifying } ; date } ; 2003 - 02 - 04 } } = true
Output: select the rows whose competition record fuzzily matches to uefa euro 2004 qualifying . there is only one such row in the table . the date record of this unqiue row is 2003  02  04 .
most_eq { all_rows ; winner ; aus } = true
Output: for the winner records of all rows , most of them fuzzily match to aus .
most_eq { all_rows ; inaba ; 7 } = true
Output: for the inaba records of all rows , most of them are equal to 7 .
and { only { filter_eq { all_rows ; college ; north dakota } } ; eq { hop { filter_eq { all_rows ; college ; north dakota } ; player } ; tim prinsen } } = true
Output: select the rows whose college record fuzzily matches to north dakota . there is only one such row in the table . the player record of this unqiue row is tim prinsen .
eq { count { filter_all { all_rows ; rank } } ; 9 } = true
Output: select the rows whose rank record is arbitrary . the number of such rows is 9 .