niryuu/sni-each-converted huggingface dataset:
eq { count { filter_eq { all_rows ; affiliation ; private christian } } ; 2 } = true
Output: select the rows whose affiliation record fuzzily matches to private christian . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; opposing team ; fiji } ; against } ; hop { filter_eq { all_rows ; opposing team ; taranaki } ; against } } = true
Output: select the rows whose opposing team record fuzzily matches to fiji . take the against record of this row . select the rows whose opposing team record fuzzily matches to taranaki . take the against record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; date ; 16 october 2012 } ; score } ; hop { filter_eq { all_rows ; date ; 8 october 2010 } ; score } } = true
Output: select the rows whose date record fuzzily matches to 16 october 2012 . take the score record of this row . select the rows whose date record fuzzily matches to 8 october 2010 . take the score record of this row . the first record is less than the second record .
less { hop { filter_eq { all_rows ; successor ; thomas tillinghast ( f ) } ; date successor seated } ; hop { filter_eq { all_rows ; successor ; robert waln ( f ) } ; date successor seated } } = true
Output: select the rows whose successor record fuzzily matches to thomas tillinghast  f  . take the date successor seated record of this row . select the rows whose successor record fuzzily matches to robert waln  f  . take the date successor seated record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; executions in persona ; 4 } } ; 2 } = true
Output: select the rows whose executions in persona record is equal to 4 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; population served } ; location } ; marcala , hon } = true
Output: select the row whose population served record of all rows is maximum . the location record of this row is marcala , hon .
round_eq { avg { all_rows ; votes } ; 13004 } = true
Output: the average of the votes record of all rows is 13004 .
eq { count { filter_eq { filter_eq { all_rows ; competition ; 1997 korea cup } ; score ; 1 goal } } ; 2 } = true
Output: select the rows whose competition record fuzzily matches to 1997 korea cup . among these rows , select the rows whose score record fuzzily matches to 1 goal . the number of such rows is 2 .
only { filter_eq { all_rows ; position ; linebacker } } = true
Output: select the rows whose position record fuzzily matches to linebacker . there is only one such row in the table .
greater { hop { filter_eq { all_rows ; airport name ; los angeles international airport } ; tonnes } ; hop { filter_eq { all_rows ; airport name ; john f kennedy international airport } ; tonnes } } = true
Output: select the rows whose airport name record fuzzily matches to los angeles international airport . take the tonnes record of this row . select the rows whose airport name record fuzzily matches to john f kennedy international airport . take the tonnes record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; elevation ( m ) } ; 1952 } = true
Output: the average of the elevation  m  record of all rows is 1952 .
less { hop { filter_eq { all_rows ; opponent in the final ; christína papadáki } ; date } ; hop { filter_eq { all_rows ; opponent in the final ; julia vakulenko } ; date } } = true
Output: select the rows whose opponent in the final record fuzzily matches to christína papadáki . take the date record of this row . select the rows whose opponent in the final record fuzzily matches to julia vakulenko . take the date record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; points ; 10 } } ; eq { hop { filter_eq { all_rows ; points ; 10 } ; entrant } ; braun tyrrell honda } } = true
Output: select the rows whose points record is equal to 10 . there is only one such row in the table . the entrant record of this unqiue row is braun tyrrell honda .
round_eq { avg { all_rows ; earnings } ; 22046805 } = true
Output: the average of the earnings record of all rows is 22046805 .
eq { count { filter_eq { all_rows ; class ; de } } ; 3 } = true
Output: select the rows whose class record fuzzily matches to de . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; keith ellison } = true
Output: select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is keith ellison .
and { only { filter_eq { all_rows ; position ; goalkeeper } } ; eq { hop { filter_eq { all_rows ; position ; goalkeeper } ; player } ; paul robinson } } = true
Output: select the rows whose position record fuzzily matches to goalkeeper . there is only one such row in the table . the player record of this unqiue row is paul robinson .
eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; title } ; the coffee machine paid for itself } = true
Output: select the row whose original air date record of all rows is 2nd maximum . the title record of this row is the coffee machine paid for itself .
eq { hop { nth_argmin { all_rows ; time ; 3 } ; rowers } ; misaki kumakura , akiko iwamoto } = true
Output: select the row whose time record of all rows is 3rd minimum . the rowers record of this row is misaki kumakura , akiko iwamoto .
round_eq { avg { filter_eq { all_rows ; artist ; morena camilleri } ; place } ; 3 } = true
Output: select the rows whose artist record fuzzily matches to morena camilleri . the average of the place record of these rows is 3 .
eq { hop { nth_argmax { all_rows ; age ; 2 } ; contestant } ; daniella filipovic } = true
Output: select the row whose age record of all rows is 2nd maximum . the contestant record of this row is daniella filipovic .
eq { hop { nth_argmin { filter_eq { all_rows ; score ; l } ; date ; 3 } ; attendance } ; 11741 } = true
Output: select the rows whose score record fuzzily matches to l . select the row whose date record of these rows is 3rd minimum . the attendance record of this row is 11741 .
most_eq { all_rows ; livery ; pacific national blue & yellow } = true
Output: for the livery records of all rows , most of them fuzzily match to pacific national blue  yellow .
eq { count { filter_eq { all_rows ; location ; nc } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to nc . the number of such rows is 3 .
only { filter_eq { all_rows ; directed by ; john kretchmer } } = true
Output: select the rows whose directed by record fuzzily matches to john kretchmer . there is only one such row in the table .
only { filter_less { filter_eq { all_rows ; country ; united states } ; money ; 140000 } } = true
Output: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose money record is less than 140000 . there is only one such row in the table .
and { only { filter_greater { filter_eq { all_rows ; score ; 0 } ; attendance ; 4000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; score ; 0 } ; attendance ; 4000 } ; home team } ; milton keynes dons } } = true
Output: select the rows whose score record fuzzily matches to 0 . among these rows , select the rows whose attendance record is greater than 4000 . there is only one such row in the table . the home team record of this unqiue row is milton keynes dons .
eq { count { filter_not_eq { filter_eq { all_rows ; venue ; beirut } ; result ; win } } ; 2 } = true
Output: select the rows whose venue record fuzzily matches to beirut . among these rows , select the rows whose result record does not match to win . the number of such rows is 2 .
and { only { filter_eq { all_rows ; score ; 4:1 } } ; and { eq { hop { filter_eq { all_rows ; score ; 4:1 } ; team 1 } ; muangthong united } ; eq { hop { filter_eq { all_rows ; score ; 4:1 } ; team 2 } ; persiwa wamena } } } = true
Output: select the rows whose score record fuzzily matches to 4:1 . there is only one such row in the table . the team 1 record of this unqiue row is muangthong united . the team 2 record of this unqiue row is persiwa wamena .
round_eq { avg { all_rows ; stages won } ; 1.6 } = true
Output: the average of the stages won record of all rows is 1.6 .
round_eq { avg { all_rows ; total prize money } ; 135000 } = true
Output: the average of the total prize money record of all rows is 135000 .
eq { count { filter_eq { all_rows ; time of day ; afternoon } } ; 3 } = true
Output: select the rows whose time of day record fuzzily matches to afternoon . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; wins ; 1 } } ; 1 } = true
Output: select the rows whose wins record is equal to 1 . the number of such rows is 1 .
only { filter_eq { all_rows ; month ; april } } = true
Output: select the rows whose month record fuzzily matches to april . there is only one such row in the table .
eq { max { filter_greater { all_rows ; erp w ; 25 } ; frequency mhz } ; 106.1 } = true
Output: select the rows whose erp w record is greater than 25 . the maximum frequency mhz record of these rows is 106.1 .
most_eq { all_rows ; webcast link ; link } = true
Output: for the webcast link records of all rows , most of them fuzzily match to link .
eq { hop { nth_argmin { all_rows ; date ; 5 } ; team } ; houston } = true
Output: select the row whose date record of all rows is 5th minimum . the team record of this row is houston .
eq { nth_max { all_rows ; candidates ; 1 } ; caleb tompkins ( dr ) 56.8 % abraham odell ( f ) 42.8 % } = true
Output: the 1st maximum candidates record of all rows is caleb tompkins  dr  56.8  abraham odell  f  42.8  .
most_less { all_rows ; time ; 7:00:00 } = true
Output: for the time records of all rows , most of them are less than 7:00:00 .
and { only { filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1898 } } ; eq { hop { filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1898 } ; incumbent } ; david e finley } } = true
Output: select the rows whose result record fuzzily matches to re  elected . among these rows , select the rows whose first elected record is equal to 1898 . there is only one such row in the table . the incumbent record of this unqiue row is david e finley .
and { greater { hop { filter_eq { all_rows ; album ; american gangster } ; 1st week sales } ; hop { filter_eq { all_rows ; album ; from nothin ' to somethin' } ; 1st week sales } } ; and { eq { hop { filter_eq { all_rows ; album ; american gangster } ; 1st week sales } ; 426000 } ; eq { hop { filter_eq { all_rows ; album ; from nothin ' to somethin' } ; 1st week sales } ; 159000 } } } = true
Output: select the rows whose album record fuzzily matches to american gangster . take the 1st week sales record of this row . select the rows whose album record fuzzily matches to from nothin  to somethin . take the 1st week sales record of this row . the first record is greater than the second record . the 1st week sales record of the first row is 426000 . the 1st week sales record of the second row is 159000 .
eq { hop { argmax { all_rows ; elevation ( m ) } ; peak } ; galdhøpiggen } = true
Output: select the row whose elevation  m  record of all rows is maximum . the peak record of this row is galdhøpiggen .
and { only { filter_greater { filter_eq { all_rows ; years ; 196 } ; total ; 600 } } ; eq { hop { filter_greater { filter_eq { all_rows ; years ; 196 } ; total ; 600 } ; name } ; joe corrigan } } = true
Output: select the rows whose years record fuzzily matches to 196 . among these rows , select the rows whose total record is greater than 600 . there is only one such row in the table . the name record of this unqiue row is joe corrigan .
eq { hop { nth_argmax { all_rows ; cores ; 2 } ; model } ; l3c } = true
Output: select the row whose cores record of all rows is 2nd maximum . the model record of this row is l3c .
round_eq { avg { all_rows ; to par } ; 0.92 } = true
Output: the average of the to par record of all rows is 0.92 .
eq { count { filter_eq { all_rows ; pos ; dnf } } ; 4 } = true
Output: select the rows whose pos record fuzzily matches to dnf . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; date ( s ) ; september 2006 } } ; 2 } = true
Output: select the rows whose date  s  record fuzzily matches to september 2006 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; round ; 2 } } ; 3 } = true
Output: select the rows whose round record is equal to 2 . the number of such rows is 3 .
and { eq { nth_max { all_rows ; money ; 2 } ; 135000 } ; eq { hop { nth_argmax { all_rows ; money ; 2 } ; player } ; fred couples } } = true
Output: the 2nd maximum money record of all rows is 135000 . the player record of the row with 2nd maximum money record is fred couples .
eq { hop { argmax { all_rows ; height } ; player } ; josé luis moltó } = true
Output: select the row whose height record of all rows is maximum . the player record of this row is josé luis moltó .
and { only { filter_greater { all_rows ; total ; 25 } } ; eq { hop { filter_greater { all_rows ; total ; 25 } ; nation } ; east germany ( gdr ) } } = true
Output: select the rows whose total record is greater than 25 . there is only one such row in the table . the nation record of this unqiue row is east germany  gdr  .
eq { count { filter_greater_eq { all_rows ; lead ; 10 % } } ; 3 } = true
Output: select the rows whose lead record is greater than or equal to 10  . the number of such rows is 3 .
eq { hop { nth_argmin { all_rows ; date ; 11 } ; field } ; cawley memorial stadium } = true
Output: select the row whose date record of all rows is 11th minimum . the field record of this row is cawley memorial stadium .
round_eq { avg { all_rows ; weeks at number one } ; 8 } = true
Output: the average of the weeks at number one record of all rows is 8 .
eq { hop { argmax { all_rows ; sack } ; player } ; craig walls } = true
Output: select the row whose sack record of all rows is maximum . the player record of this row is craig walls .
round_eq { avg { filter_greater { all_rows ; total ; 16.0 } ; public vote % } ; 10.75 % } = true
Output: select the rows whose total record is greater than 16.0 . the average of the public vote  record of these rows is 10.75  .
most_less { all_rows ; time ; 54.0 } = true
Output: for the time records of all rows , most of them are less than 54.0 .
round_eq { avg { all_rows ; floor } ; 60.589 } = true
Output: the average of the floor record of all rows is 60.589 .
and { only { filter_eq { filter_eq { all_rows ; tournament ; italy } ; opponent ; marcel granollers } } ; eq { hop { filter_eq { filter_eq { all_rows ; tournament ; italy } ; opponent ; marcel granollers } ; date } ; 30 may 2006 } } = true
Output: select the rows whose tournament record fuzzily matches to italy . among these rows , select the rows whose opponent record fuzzily matches to marcel granollers . there is only one such row in the table . the date record of this unqiue row is 30 may 2006 .
eq { count { filter_eq { all_rows ; high assists ; rafer alston } } ; 3 } = true
Output: select the rows whose high assists record fuzzily matches to rafer alston . the number of such rows is 3 .
eq { hop { argmax { all_rows ; inhabitants } ; municipality } ; trento } = true
Output: select the row whose inhabitants record of all rows is maximum . the municipality record of this row is trento .
eq { count { filter_greater_eq { all_rows ; laps ; 60 } } ; 4 } = true
Output: select the rows whose laps record is greater than or equal to 60 . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; state / territory ; queensland } } ; 6 } = true
Output: select the rows whose state  territory record fuzzily matches to queensland . the number of such rows is 6 .
eq { count { filter_eq { all_rows ; label ; astralwerks records } } ; 2 } = true
Output: select the rows whose label record fuzzily matches to astralwerks records . the number of such rows is 2 .
all_eq { filter_eq { all_rows ; location ; china } ; status ; valid } = true
Output: select the rows whose location record fuzzily matches to china . for the status records of these rows , all of them fuzzily match to valid .
eq { diff { hop { filter_eq { all_rows ; player ; brad lamb } ; round } ; hop { filter_eq { all_rows ; player ; mark maddox } ; round } } ; -1 } = true
Output: select the rows whose player record fuzzily matches to brad lamb . take the round record of this row . select the rows whose player record fuzzily matches to mark maddox . take the round record of this row . the second record is 1 larger than the first record .
and { only { filter_eq { all_rows ; songwriter ( s ) ; wallis willis } } ; eq { hop { filter_eq { all_rows ; songwriter ( s ) ; wallis willis } ; title } ; swing low , sweet chariot } } = true
Output: select the rows whose songwriter  s  record fuzzily matches to wallis willis . there is only one such row in the table . the title record of this unqiue row is swing low , sweet chariot .
eq { count { filter_all { all_rows ; year } } ; 5 } = true
Output: select the rows whose year record is arbitrary . the number of such rows is 5 .
eq { hop { nth_argmin { all_rows ; introduced ; 2 } ; aircraft } ; boeing 737 - 300 } = true
Output: select the row whose introduced record of all rows is 2nd minimum . the aircraft record of this row is boeing 737  300 .
eq { hop { nth_argmax { all_rows ; pick ; 2 } ; player } ; spencer wadsworth } = true
Output: select the row whose pick record of all rows is 2nd maximum . the player record of this row is spencer wadsworth .
eq { count { filter_greater { all_rows ; gold ; 20 } } ; 3 } = true
Output: select the rows whose gold record is greater than 20 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; date introduced ; june } } ; 5 } = true
Output: select the rows whose date introduced record fuzzily matches to june . the number of such rows is 5 .
eq { hop { nth_argmin { all_rows ; date ; 3 } ; centerfold model } ; sandra hubby } = true
Output: select the row whose date record of all rows is 3rd minimum . the centerfold model record of this row is sandra hubby .
eq { hop { nth_argmax { all_rows ; points ; 2 } ; song } ; are you sure } = true
Output: select the row whose points record of all rows is 2nd maximum . the song record of this row is are you sure .
eq { hop { argmax { filter_eq { all_rows ; county ; 28 greene } ; enrollment } ; location } ; bloomfield } = true
Output: select the rows whose county record fuzzily matches to 28 greene . select the row whose enrollment record of these rows is maximum . the location record of this row is bloomfield .
eq { count { filter_eq { all_rows ; manner of departure ; resigned } } ; 6 } = true
Output: select the rows whose manner of departure record fuzzily matches to resigned . the number of such rows is 6 .
and { only { filter_greater { all_rows ; wickets ; 700 } } ; eq { hop { filter_greater { all_rows ; wickets ; 700 } ; name } ; shane warne } } = true
Output: select the rows whose wickets record is greater than 700 . there is only one such row in the table . the name record of this unqiue row is shane warne .
eq { count { filter_eq { all_rows ; status ; six nations } } ; 5 } = true
Output: select the rows whose status record fuzzily matches to six nations . the number of such rows is 5 .
most_eq { all_rows ; drew ; 0 } = true
Output: for the drew records of all rows , most of them are equal to 0 .
eq { count { filter_less { filter_greater { all_rows ; home team score ; 15.0 } ; crowd ; 10000 } } ; 2 } = true
Output: select the rows whose home team score record is greater than 15.0 . among these rows , select the rows whose crowd record is less than 10000 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; result ; re - elected } } ; 9 } = true
Output: select the rows whose result record fuzzily matches to re  elected . the number of such rows is 9 .
eq { hop { nth_argmax { all_rows ; result ; 3 } ; opponent } ; arizona cardinals } = true
Output: select the row whose result record of all rows is 3rd maximum . the opponent record of this row is arizona cardinals .
most_eq { all_rows ; affiliation ; private } = true
Output: for the affiliation records of all rows , most of them fuzzily match to private .
all_eq { all_rows ; aspect ; 4:3 } = true
Output: for the aspect records of all rows , all of them fuzzily match to 4:3 .
round_eq { avg { all_rows ; score } ; 34.8 } = true
Output: the average of the score record of all rows is 34.8 .
eq { hop { argmax { all_rows ; swansea win } ; competition } ; league } = true
Output: select the row whose swansea win record of all rows is maximum . the competition record of this row is league .
round_eq { avg { all_rows ; laps } ; 102.9 } = true
Output: the average of the laps record of all rows is 102.9 .
less { hop { filter_eq { all_rows ; country ; belgium } ; total } ; hop { filter_eq { all_rows ; country ; brazil } ; total } } = true
Output: select the rows whose country record fuzzily matches to belgium . take the total record of this row . select the rows whose country record fuzzily matches to brazil . take the total record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; silver } ; nation } ; cuba } = true
Output: select the row whose silver record of all rows is maximum . the nation record of this row is cuba .
only { filter_eq { all_rows ; result ; 5th } } = true
Output: select the rows whose result record fuzzily matches to 5th . there is only one such row in the table .
eq { count { filter_eq { all_rows ; lost ; 1 } } ; 5 } = true
Output: select the rows whose lost record is equal to 1 . the number of such rows is 5 .
eq { hop { argmax { all_rows ; gold } ; nation } ; cuba } = true
Output: select the row whose gold record of all rows is maximum . the nation record of this row is cuba .
eq { hop { nth_argmax { all_rows ; total ; 2 } ; nation } ; mexico } = true
Output: select the row whose total record of all rows is 2nd maximum . the nation record of this row is mexico .
eq { hop { nth_argmax { all_rows ; enrollment ; 1 } ; school } ; carson } = true
Output: select the row whose enrollment record of all rows is 1st maximum . the school record of this row is carson .
and { only { filter_greater { filter_eq { all_rows ; venue ; melbourne } ; goals ; 0 } } ; eq { hop { filter_greater { filter_eq { all_rows ; venue ; melbourne } ; goals ; 0 } ; date } ; 1956 - 11 - 27 } } = true
Output: select the rows whose venue record fuzzily matches to melbourne . among these rows , select the rows whose goals record is greater than 0 . there is only one such row in the table . the date record of this unqiue row is 1956  11  27 .
round_eq { avg { filter_not_eq { all_rows ; candidates ; unopposed } ; candidates } ; 72 } = true
Output: select the rows whose candidates record does not match to unopposed . the average of the candidates record of these rows is 72 .
most_eq { all_rows ; power ( kw ) ; 5 kw } = true
Output: for the power  kw  records of all rows , most of them fuzzily match to 5 kw .
round_eq { sum { filter_eq { all_rows ; date ; 1873 } ; no built } ; 14 } = true
Output: select the rows whose date record is equal to 1873 . the sum of the no built record of these rows is 14 .
and { only { filter_eq { all_rows ; series ; 1 - 13 } } ; eq { hop { filter_eq { all_rows ; series ; 1 - 13 } ; actor } ; david suchet } } = true
Output: select the rows whose series record fuzzily matches to 1  13 . there is only one such row in the table . the actor record of this unqiue row is david suchet .
eq { hop { argmax { all_rows ; per capita withdrawal } ; } ; turkmenistan } = true
Output: select the row whose per capita withdrawal record of all rows is maximum . the record of this row is turkmenistan .
greater { hop { filter_eq { all_rows ; institution ; university of texas at austin } ; enrollment } ; hop { filter_eq { all_rows ; institution ; texas christian university } ; enrollment } } = true
Output: select the rows whose institution record fuzzily matches to university of texas at austin . take the enrollment record of this row . select the rows whose institution record fuzzily matches to texas christian university . take the enrollment record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; result ; won } } ; 8 } = true
Output: select the rows whose result record fuzzily matches to won . the number of such rows is 8 .
greater { hop { filter_eq { all_rows ; season ; 2009 - 10 } ; goals } ; hop { filter_eq { all_rows ; season ; 2012 - 13 } ; goals } } = true
Output: select the rows whose season record fuzzily matches to 2009  10 . take the goals record of this row . select the rows whose season record fuzzily matches to 2012  13 . take the goals record of this row . the first record is greater than the second record .
all_eq { all_rows ; motor ; castor 4b } = true
Output: for the motor records of all rows , all of them fuzzily match to castor 4b .
round_eq { avg { all_rows ; area } ; over 2700 } = true
Output: the average of the area record of all rows is over 2700 .
most_eq { all_rows ; launch year ; 2004 } = true
Output: for the launch year records of all rows , most of them are equal to 2004 .
eq { hop { argmax { all_rows ; rank } ; name } ; environmental sustainability index } = true
Output: select the row whose rank record of all rows is maximum . the name record of this row is environmental sustainability index .
eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; institution } ; college of mount st joseph } = true
Output: select the row whose enrollment record of all rows is 2nd maximum . the institution record of this row is college of mount st joseph .
greater { hop { filter_eq { all_rows ; season ; 2003 / 04 } ; goals } ; hop { filter_eq { all_rows ; season ; 2004 / 05 } ; goals } } = true
Output: select the rows whose season record fuzzily matches to 2003  04 . take the goals record of this row . select the rows whose season record fuzzily matches to 2004  05 . take the goals record of this row . the first record is greater than the second record .
most_eq { all_rows ; region ; usa } = true
Output: for the region records of all rows , most of them fuzzily match to usa .
eq { hop { argmin { all_rows ; pick } ; player } ; ken hicks } = true
Output: select the row whose pick record of all rows is minimum . the player record of this row is ken hicks .
eq { count { filter_eq { all_rows ; gymnast ; kor } } ; 2 } = true
Output: select the rows whose gymnast record fuzzily matches to kor . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; high rebounds ; m cage } } ; 3 } = true
Output: select the rows whose high rebounds record fuzzily matches to m cage . the number of such rows is 3 .
eq { count { filter_less { filter_eq { all_rows ; class ; d } ; frequency mhz ; 90.0 } } ; 2 } = true
Output: select the rows whose class record fuzzily matches to d . among these rows , select the rows whose frequency mhz record is less than 90.0 . the number of such rows is 2 .
round_eq { avg { all_rows ; score } ; 1.4 } = true
Output: the average of the score record of all rows is 1.4 .
eq { count { filter_eq { filter_eq { all_rows ; opponents ; fulham } ; result ; 1-1 } } ; 2 } = true
Output: select the rows whose opponents record fuzzily matches to fulham . among these rows , select the rows whose result record fuzzily matches to 11 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; county ; rankin } } ; 2 } = true
Output: select the rows whose county record fuzzily matches to rankin . the number of such rows is 2 .
eq { hop { argmax { all_rows ; score } ; date } ; june 24 } = true
Output: select the row whose score record of all rows is maximum . the date record of this row is june 24 .
round_eq { avg { all_rows ; attendance } ; 46,203 } = true
Output: the average of the attendance record of all rows is 46,203 .
round_eq { avg { filter_eq { all_rows ; top scorer ; sergei mozyakin } ; top scorer } ; 72.67 } = true
Output: select the rows whose top scorer record fuzzily matches to sergei mozyakin . the average of the top scorer record of these rows is 72.67 .
and { only { filter_greater { all_rows ; weeks ; 600 } } ; eq { hop { filter_greater { all_rows ; weeks ; 600 } ; player } ; tiger woods } } = true
Output: select the rows whose weeks record is greater than 600 . there is only one such row in the table . the player record of this unqiue row is tiger woods .
most_eq { all_rows ; away team ; melbourne } = true
Output: for the away team records of all rows , most of them fuzzily match to melbourne .
eq { count { filter_less { filter_less { all_rows ; away team score ; 20 } ; crowd ; 20000 } } ; 2 } = true
Output: select the rows whose away team score record is less than 20 . among these rows , select the rows whose crowd record is less than 20000 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; original airdate ; may } } ; eq { hop { filter_eq { all_rows ; original airdate ; may } ; episode title } ; secret soviet bases } } = true
Output: select the rows whose original airdate record fuzzily matches to may . there is only one such row in the table . the episode title record of this unqiue row is secret soviet bases .
less { hop { filter_eq { all_rows ; record company ; rca } ; year of recording } ; hop { filter_eq { all_rows ; record company ; abc classics } ; year of recording } } = true
Output: select the rows whose record company record fuzzily matches to rca . take the year of recording record of this row . select the rows whose record company record fuzzily matches to abc classics . take the year of recording record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; country ; brazil } ; change ( 2011 to 2012 ) } ; hop { filter_eq { all_rows ; country ; canada } ; change ( 2011 to 2012 ) } } = true
Output: select the rows whose country record fuzzily matches to brazil . take the change  2011 to 2012  record of this row . select the rows whose country record fuzzily matches to canada . take the change  2011 to 2012  record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; title ; dimension twist } ; original air date } ; hop { filter_eq { all_rows ; title ; team impossible } ; original air date } } = true
Output: select the rows whose title record fuzzily matches to dimension twist . take the original air date record of this row . select the rows whose title record fuzzily matches to team impossible . take the original air date record of this row . the first record is less than the second record .
less { hop { filter_eq { all_rows ; event ; 2010 vancouver } ; individual } ; hop { filter_eq { all_rows ; event ; 1998 nagano - nozawa } ; individual } } = true
Output: select the rows whose event record fuzzily matches to 2010 vancouver . take the individual record of this row . select the rows whose event record fuzzily matches to 1998 nagano  nozawa . take the individual record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; goals } ; season } ; 2005 - 2006 } = true
Output: select the row whose goals record of all rows is maximum . the season record of this row is 2005  2006 .
round_eq { sum { all_rows ; races } ; 190 } = true
Output: the sum of the races record of all rows is 190 .
eq { hop { argmax { all_rows ; attendance } ; tie no } ; 51 } = true
Output: select the row whose attendance record of all rows is maximum . the tie no record of this row is 51 .
eq { count { filter_eq { filter_eq { all_rows ; joined ; 1996 } ; location ; tennessee } } ; 4 } = true
Output: select the rows whose joined record is equal to 1996 . among these rows , select the rows whose location record fuzzily matches to tennessee . the number of such rows is 4 .
round_eq { avg { all_rows ; viewers } ; 880000 } = true
Output: the average of the viewers record of all rows is 880000 .
round_eq { avg { all_rows ; pick } ; 154 } = true
Output: the average of the pick record of all rows is 154 .
eq { hop { argmax { all_rows ; capacity } ; stadium } ; stadio marcantonio bentegodi } = true
Output: select the row whose capacity record of all rows is maximum . the stadium record of this row is stadio marcantonio bentegodi .
most_greater { all_rows ; won ; 10 } = true
Output: for the won records of all rows , most of them are greater than 10 .
eq { count { filter_eq { all_rows ; release price ( usd ) ; oem } } ; 5 } = true
Output: select the rows whose release price  usd  record fuzzily matches to oem . the number of such rows is 5 .
round_eq { sum { all_rows ; wins } ; 0 } = true
Output: the sum of the wins record of all rows is 0 .
and { only { filter_greater { all_rows ; capacity ; 84000 } } ; eq { hop { filter_greater { all_rows ; capacity ; 84000 } ; stadium } ; soccer city } } = true
Output: select the rows whose capacity record is greater than 84000 . there is only one such row in the table . the stadium record of this unqiue row is soccer city .
eq { count { filter_eq { all_rows ; venue ; kuala lumpur } } ; 3 } = true
Output: select the rows whose venue record fuzzily matches to kuala lumpur . the number of such rows is 3 .
eq { max { filter_eq { all_rows ; location attendance ; target center } ; high points } ; sebastian telfair ( 21 ) } = true
Output: select the rows whose location attendance record fuzzily matches to target center . the maximum high points record of these rows is sebastian telfair  21  .
and { only { filter_eq { all_rows ; density ; 33 } } ; eq { hop { filter_eq { all_rows ; density ; 33 } ; english name } ; luobei county } } = true
Output: select the rows whose density record is equal to 33 . there is only one such row in the table . the english name record of this unqiue row is luobei county .
eq { hop { nth_argmin { all_rows ; per capita income ; 2 } ; county } ; piscataquis } = true
Output: select the row whose per capita income record of all rows is 2nd minimum . the county record of this row is piscataquis .
and { only { filter_eq { all_rows ; competition ; friendly match } } ; eq { hop { filter_eq { all_rows ; competition ; friendly match } ; date } ; 23 march 1993 } } = true
Output: select the rows whose competition record fuzzily matches to friendly match . there is only one such row in the table . the date record of this unqiue row is 23 march 1993 .
and { only { filter_eq { all_rows ; premio ; mtv europe music awards } } ; and { eq { hop { filter_eq { all_rows ; premio ; mtv europe music awards } ; año } ; 2011 } ; eq { hop { filter_eq { all_rows ; premio ; mtv europe music awards } ; resultado } ; nominate } } } = true
Output: select the rows whose premio record fuzzily matches to mtv europe music awards . there is only one such row in the table . the año record of this unqiue row is 2011 . the resultado record of this unqiue row is nominate .
eq { count { filter_eq { filter_eq { all_rows ; date ; december } ; game site ; rich stadium } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to december . among these rows , select the rows whose game site record fuzzily matches to rich stadium . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; constructor ; minardi - cosworth } } ; 2 } = true
Output: select the rows whose constructor record fuzzily matches to minardi  cosworth . the number of such rows is 2 .
and { only { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } } ; eq { hop { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } ; date } ; april 21 } } = true
Output: select the rows whose high assists record fuzzily matches to steve blake . among these rows , select the rows whose score record fuzzily matches to w . there is only one such row in the table . the date record of this unqiue row is april 21 .
all_eq { all_rows ; score ; 1 goal } = true
Output: for the score records of all rows , all of them fuzzily match to 1 goal .
eq { count { filter_eq { filter_eq { all_rows ; year ; 2010 } ; recipient ; the suite life on deck } } ; 2 } = true
Output: select the rows whose year record is equal to 2010 . among these rows , select the rows whose recipient record fuzzily matches to the suite life on deck . the number of such rows is 2 .
and { only { filter_eq { all_rows ; goal difference ; -1 } } ; eq { hop { filter_eq { all_rows ; goal difference ; -1 } ; club } ; ce sabadell fc } } = true
Output: select the rows whose goal difference record is equal to 1 . there is only one such row in the table . the club record of this unqiue row is ce sabadell fc .
eq { hop { nth_argmax { all_rows ; frequency mhz ; 3 } ; erp kw } ; 3 } = true
Output: select the row whose frequency mhz record of all rows is 3rd maximum . the erp kw record of this row is 3 .
eq { hop { argmax { all_rows ; imports } ; country } ; china } = true
Output: select the row whose imports record of all rows is maximum . the country record of this row is china .
eq { count { filter_eq { filter_eq { all_rows ; event ; ufc } ; time ; 5:00 } } ; 4 } = true
Output: select the rows whose event record fuzzily matches to ufc . among these rows , select the rows whose time record fuzzily matches to 5:00 . the number of such rows is 4 .
eq { hop { argmax { all_rows ; points } ; club } ; pontyclun rfc } = true
Output: select the row whose points record of all rows is maximum . the club record of this row is pontyclun rfc .
eq { count { filter_eq { all_rows ; affiliation ; public junior college } } ; 7 } = true
Output: select the rows whose affiliation record fuzzily matches to public junior college . the number of such rows is 7 .
and { only { filter_eq { all_rows ; winner ; john bowe } } ; eq { hop { filter_eq { all_rows ; winner ; john bowe } ; race title } ; winton } } = true
Output: select the rows whose winner record fuzzily matches to john bowe . there is only one such row in the table . the race title record of this unqiue row is winton .
and { only { filter_eq { all_rows ; silver ; brigitte wolf } } ; eq { hop { filter_eq { all_rows ; silver ; brigitte wolf } ; year } ; 2000 } } = true
Output: select the rows whose silver record fuzzily matches to brigitte wolf . there is only one such row in the table . the year record of this unqiue row is 2000 .
most_eq { all_rows ; printer ; avery dennison } = true
Output: for the printer records of all rows , most of them fuzzily match to avery dennison .
and { only { filter_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; porto } } ; eq { hop { filter_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; porto } ; clubs } ; porto } } = true
Output: select the rows whose seasons at this level record fuzzily matches to 27 seasons . among these rows , select the rows whose settlements record fuzzily matches to porto . there is only one such row in the table . the clubs record of this unqiue row is porto .
eq { hop { argmax { all_rows ; seconds } ; name } ; kerstin jürgens - szymkowiak } = true
Output: select the row whose seconds record of all rows is maximum . the name record of this row is kerstin jürgens  szymkowiak .
eq { count { filter_eq { all_rows ; status ; test match } } ; 2 } = true
Output: select the rows whose status record fuzzily matches to test match . the number of such rows is 2 .
eq { count { filter_all { all_rows ; institution } } ; 12 } = true
Output: select the rows whose institution record is arbitrary . the number of such rows is 12 .
eq { count { filter_eq { all_rows ; score ; 71 } } ; 11 } = true
Output: select the rows whose score record is equal to 71 . the number of such rows is 11 .
eq { count { filter_eq { filter_eq { all_rows ; location ; ga } ; champion ; emory } } ; 2 } = true
Output: select the rows whose location record fuzzily matches to ga . among these rows , select the rows whose champion record fuzzily matches to emory . the number of such rows is 2 .
eq { count { filter_greater_eq { filter_greater_eq { all_rows ; goals against ; 40 } ; goals for ; 40 } } ; 6 } = true
Output: select the rows whose goals against record is greater than or equal to 40 . among these rows , select the rows whose goals for record is greater than or equal to 40 . the number of such rows is 6 .
eq { count { filter_eq { all_rows ; transmission ; 8 - speed } } ; 6 } = true
Output: select the rows whose transmission record fuzzily matches to 8  speed . the number of such rows is 6 .
eq { hop { argmax { all_rows ; score } ; date } ; 9 october 2012 } = true
Output: select the row whose score record of all rows is maximum . the date record of this row is 9 october 2012 .
eq { count { filter_eq { all_rows ; lyricist ; sameer } } ; 3 } = true
Output: select the rows whose lyricist record fuzzily matches to sameer . the number of such rows is 3 .
greater { hop { filter_eq { all_rows ; population metro area in millions ; 5.3 } ; population metro area in millions } ; hop { filter_eq { all_rows ; population metro area in millions ; 3.9 } ; population metro area in millions } } = true
Output: select the rows whose population metro area in millions record fuzzily matches to 5.3 . take the population metro area in millions record of this row . select the rows whose population metro area in millions record fuzzily matches to 3.9 . take the population metro area in millions record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; ends won } ; skip ( club ) } ; chris gardner ( renfrew ) } = true
Output: select the row whose ends won record of all rows is maximum . the skip  club  record of this row is chris gardner  renfrew  .
eq { count { filter_eq { filter_greater { all_rows ; total ; 30 } ; style ; jive } } ; 2 } = true
Output: select the rows whose total record is greater than 30 . among these rows , select the rows whose style record fuzzily matches to jive . the number of such rows is 2 .
round_eq { sum { all_rows ; quantity made } ; 151 } = true
Output: the sum of the quantity made record of all rows is 151 .
round_eq { sum { filter_eq { all_rows ; engine ; ferrari straight-4 } ; points } ; 31 } = true
Output: select the rows whose engine record fuzzily matches to ferrari straight4 . the sum of the points record of these rows is 31 .
eq { count { filter_less { all_rows ; attendance ; 35000 } } ; 2 } = true
Output: select the rows whose attendance record is less than 35000 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; capacity ; 15042 } } ; eq { hop { filter_eq { all_rows ; capacity ; 15042 } ; home venue } ; sangju civic stadium } } = true
Output: select the rows whose capacity record is equal to 15042 . there is only one such row in the table . the home venue record of this unqiue row is sangju civic stadium .
round_eq { avg { all_rows ; speed } ; 97.34 } = true
Output: the average of the speed record of all rows is 97.34 .
eq { hop { nth_argmax { all_rows ; kerry ; 2 } ; county } ; fairfield } = true
Output: select the row whose kerry record of all rows is 2nd maximum . the county record of this row is fairfield .
and { only { filter_eq { all_rows ; gold ; 1 } } ; eq { hop { filter_eq { all_rows ; gold ; 1 } ; nation } ; switzerland } } = true
Output: select the rows whose gold record is equal to 1 . there is only one such row in the table . the nation record of this unqiue row is switzerland .
and { only { filter_eq { all_rows ; director ; hassard short } } ; eq { hop { filter_eq { all_rows ; director ; hassard short } ; date } ; june 3 , 1931 } } = true
Output: select the rows whose director record fuzzily matches to hassard short . there is only one such row in the table . the date record of this unqiue row is june 3 , 1931 .
eq { count { filter_eq { all_rows ; result ; re - elected } } ; 5 } = true
Output: select the rows whose result record fuzzily matches to re  elected . the number of such rows is 5 .
and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; tournament } ; eckental , germany } } = true
Output: select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the tournament record of this unqiue row is eckental , germany .
eq { min { all_rows ; result } ; 2nd } = true
Output: the minimum result record of all rows is 2nd .
most_eq { all_rows ; top division titles ; 0 } = true
Output: for the top division titles records of all rows , most of them are equal to 0 .
all_eq { all_rows ; class ; 31 } = true
Output: for the class records of all rows , all of them are equal to 31 .
less { hop { filter_eq { all_rows ; opponent ; montreal maroons } ; march } ; hop { filter_eq { all_rows ; opponent ; boston bruins } ; march } } = true
Output: select the rows whose opponent record fuzzily matches to montreal maroons . take the march record of this row . select the rows whose opponent record fuzzily matches to boston bruins . take the march record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; date ; february } } ; 4 } = true
Output: select the rows whose date record fuzzily matches to february . the number of such rows is 4 .
and { only { filter_eq { all_rows ; college ; florida state } } ; eq { hop { filter_eq { all_rows ; college ; florida state } ; player } ; nick o'leary } } = true
Output: select the rows whose college record fuzzily matches to florida state . there is only one such row in the table . the player record of this unqiue row is nick oleary .
eq { hop { nth_argmax { all_rows ; population ; 3 } ; city } ; beijing } = true
Output: select the row whose population record of all rows is 3rd maximum . the city record of this row is beijing .
round_eq { sum { all_rows ; issue price } ; 395.1 } = true
Output: the sum of the issue price record of all rows is 395.1 .
most_eq { all_rows ; current status ; fox affiliate } = true
Output: for the current status records of all rows , most of them fuzzily match to fox affiliate .
and { only { filter_greater { all_rows ; time ; 7:00:00 } } ; eq { hop { filter_greater { all_rows ; time ; 7:00:00 } ; country } ; iraq } } = true
Output: select the rows whose time record is greater than 7:00:00 . there is only one such row in the table . the country record of this unqiue row is iraq .
round_eq { avg { all_rows ; goals } ; 18.83 } = true
Output: the average of the goals record of all rows is 18.83 .
eq { hop { nth_argmax { all_rows ; pick ; 2 } ; player } ; igor larionov } = true
Output: select the row whose pick record of all rows is 2nd maximum . the player record of this row is igor larionov .
eq { hop { argmax { all_rows ; height in ft } ; player } ; sampson , ralph ralph sampson } = true
Output: select the row whose height in ft record of all rows is maximum . the player record of this row is sampson , ralph ralph sampson .
greater { hop { filter_eq { all_rows ; county name ; warrick } ; population ( 2010 ) ( rank ) } ; hop { filter_eq { all_rows ; county name ; perry } ; population ( 2010 ) ( rank ) } } = true
Output: select the rows whose county name record fuzzily matches to warrick . take the population  2010   rank  record of this row . select the rows whose county name record fuzzily matches to perry . take the population  2010   rank  record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; tdp ; 65 w } } ; 7 } = true
Output: select the rows whose tdp record fuzzily matches to 65 w . the number of such rows is 7 .
and { eq { hop { filter_eq { all_rows ; player ; mark eaton } ; nationality } ; hop { filter_eq { all_rows ; player ; jerry eaves } ; nationality } } ; and { eq { hop { filter_eq { all_rows ; player ; mark eaton } ; nationality } ; united states } ; eq { hop { filter_eq { all_rows ; player ; jerry eaves } ; nationality } ; united states } } } = true
Output: select the rows whose player record fuzzily matches to mark eaton . take the nationality record of this row . select the rows whose player record fuzzily matches to jerry eaves . take the nationality record of this row . the first record fuzzily matches to the second record . the nationality record of the first row is united states . the nationality record of the second row is united states .
round_eq { sum { all_rows ; attendance } ; 554905 } = true
Output: the sum of the attendance record of all rows is 554905 .
eq { count { filter_eq { all_rows ; points ; 130 } } ; 2 } = true
Output: select the rows whose points record is equal to 130 . the number of such rows is 2 .