niryuu/sni-each-converted huggingface dataset:
eq { count { filter_eq { all_rows ; laps ; 200 } } ; 4 } = true
Output: select the rows whose laps record is equal to 200 . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; losses ; 13 } } ; 4 } = true
Output: select the rows whose losses record is equal to 13 . the number of such rows is 4 .
most_eq { all_rows ; surface ; hard } = true
Output: for the surface records of all rows , most of them fuzzily match to hard .
greater { hop { filter_eq { all_rows ; urban area ; madrid } ; population } ; hop { filter_eq { all_rows ; urban area ; porto } ; population } } = true
Output: select the rows whose urban area record fuzzily matches to madrid . take the population record of this row . select the rows whose urban area record fuzzily matches to porto . take the population record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; length ( male ) ; 3 } ; scientific name } ; trioceros jacksonii } = true
Output: select the row whose length  male  record of all rows is 3rd maximum . the scientific name record of this row is trioceros jacksonii .
most_eq { all_rows ; surface ; clay } = true
Output: for the surface records of all rows , most of them fuzzily match to clay .
and { only { filter_eq { all_rows ; nationality ; jamaica } } ; eq { hop { filter_eq { all_rows ; nationality ; jamaica } ; athlete } ; michael blackwood } } = true
Output: select the rows whose nationality record fuzzily matches to jamaica . there is only one such row in the table . the athlete record of this unqiue row is michael blackwood .
all_eq { all_rows ; result ; re - elected } = true
Output: for the result records of all rows , all of them fuzzily match to re  elected .
less { hop { filter_eq { all_rows ; designation hd ; hd 178428 } ; arrival date } ; hop { filter_eq { all_rows ; designation hd ; hd 186408 } ; arrival date } } = true
Output: select the rows whose designation hd record fuzzily matches to hd 178428 . take the arrival date record of this row . select the rows whose designation hd record fuzzily matches to hd 186408 . take the arrival date record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; total } ; nation } ; soviet union } = true
Output: select the row whose total record of all rows is maximum . the nation record of this row is soviet union .
eq { count { filter_eq { all_rows ; surface ; clay } } ; 4 } = true
Output: select the rows whose surface record fuzzily matches to clay . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; pavement ; asphalt } } ; 9 } = true
Output: select the rows whose pavement record fuzzily matches to asphalt . the number of such rows is 9 .
eq { hop { argmax { all_rows ; height } ; player } ; ferhat akdeniz } = true
Output: select the row whose height record of all rows is maximum . the player record of this row is ferhat akdeniz .
eq { hop { argmax { all_rows ; capacity } ; stadium } ; anz stadium } = true
Output: select the row whose capacity record of all rows is maximum . the stadium record of this row is anz stadium .
round_eq { sum { all_rows ; winners share } ; 444286 } = true
Output: the sum of the winners share record of all rows is 444286 .
greater { hop { filter_eq { all_rows ; venue ; princes park } ; home team score } ; hop { filter_eq { all_rows ; venue ; brunswick street oval } ; home team score } } = true
Output: select the rows whose venue record fuzzily matches to princes park . take the home team score record of this row . select the rows whose venue record fuzzily matches to brunswick street oval . take the home team score record of this row . the first record is greater than the second record .
eq { count { filter_eq { filter_eq { all_rows ; directed by ; brad turner } ; written by ; manny coto & brannon braga } } ; 3 } = true
Output: select the rows whose directed by record fuzzily matches to brad turner . among these rows , select the rows whose written by record fuzzily matches to manny coto  brannon braga . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; area ( km square ) ; 3 } ; name of county } ; pest } = true
Output: select the row whose area  km square  record of all rows is 3rd maximum . the name of county record of this row is pest .
most_greater { filter_eq { all_rows ; country ; united states } ; events ; 21 } = true
Output: select the rows whose country record fuzzily matches to united states . for the events records of these rows , most of them are greater than 21 .
most_eq { all_rows ; venue ; vienna , austria } = true
Output: for the venue records of all rows , most of them fuzzily match to vienna , austria .
eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 0 } } ; 2 } = true
Output: select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 0 . the number of such rows is 2 .
most_less { filter_eq { all_rows ; written by ; dan schneider } ; us viewers ( millions ) ; 5 million } = true
Output: select the rows whose written by record fuzzily matches to dan schneider . for the us viewers  millions  records of these rows , most of them are less than 5 million .
eq { count { filter_eq { filter_less { all_rows ; year ; 1998 } ; country ; sweden } } ; 1 } = true
Output: select the rows whose year record is less than 1998 . among these rows , select the rows whose country record fuzzily matches to sweden . the number of such rows is 1 .
eq { count { filter_less { all_rows ; viewers ( households in millions ) ; 18 } } ; 3 } = true
Output: select the rows whose viewers  households in millions  record is less than 18 . the number of such rows is 3 .
and { eq { hop { filter_eq { all_rows ; team 1 ; elche } ; 2nd leg } ; hop { filter_eq { all_rows ; team 1 ; numancia } ; 2nd leg } } ; and { eq { hop { filter_eq { all_rows ; team 1 ; elche } ; 2nd leg } ; 0 - 2 } ; eq { hop { filter_eq { all_rows ; team 1 ; numancia } ; 2nd leg } ; 0 - 2 } } } = true
Output: select the rows whose team 1 record fuzzily matches to elche . take the 2nd leg record of this row . select the rows whose team 1 record fuzzily matches to numancia . take the 2nd leg record of this row . the first record fuzzily matches to the second record . the 2nd leg record of the first row is 0  2 . the 2nd leg record of the second row is 0  2 .
eq { count { filter_eq { all_rows ; owned since ; 2011 } } ; 9 } = true
Output: select the rows whose owned since record is equal to 2011 . the number of such rows is 9 .
eq { count { filter_eq { all_rows ; bowling style ; left arm orthodox spin } } ; 1 } = true
Output: select the rows whose bowling style record fuzzily matches to left arm orthodox spin . the number of such rows is 1 .
and { only { filter_eq { all_rows ; opponent ; la salle } } ; eq { hop { filter_eq { all_rows ; opponent ; la salle } ; year } ; 2013 } } = true
Output: select the rows whose opponent record fuzzily matches to la salle . there is only one such row in the table . the year record of this unqiue row is 2013 .
eq { count { filter_eq { all_rows ; tv time ; fox } } ; 2 } = true
Output: select the rows whose tv time record fuzzily matches to fox . the number of such rows is 2 .
eq { count { filter_eq { filter_eq { all_rows ; service ; marine corps } ; rank ; private } } ; 5 } = true
Output: select the rows whose service record fuzzily matches to marine corps . among these rows , select the rows whose rank record fuzzily matches to private . the number of such rows is 5 .
eq { hop { argmax { all_rows ; gold } ; nation } ; canada ( can ) } = true
Output: select the row whose gold record of all rows is maximum . the nation record of this row is canada  can  .
eq { count { filter_eq { all_rows ; directed by ; levie isaacks } } ; 5 } = true
Output: select the rows whose directed by record fuzzily matches to levie isaacks . the number of such rows is 5 .
round_eq { avg { filter_less_eq { all_rows ; rank ; 3 } ; time } ; 10.73 } = true
Output: select the rows whose rank record is less than or equal to 3 . the average of the time record of these rows is 10.73 .
eq { count { filter_eq { all_rows ; winning driver ; roberval andrade } } ; 4 } = true
Output: select the rows whose winning driver record fuzzily matches to roberval andrade . the number of such rows is 4 .
and { only { filter_eq { all_rows ; club team ; plymouth whalers ( ohl ) } } ; eq { hop { filter_eq { all_rows ; club team ; plymouth whalers ( ohl ) } ; player } ; stefan noesen } } = true
Output: select the rows whose club team record fuzzily matches to plymouth whalers  ohl  . there is only one such row in the table . the player record of this unqiue row is stefan noesen .
most_eq { all_rows ; method ; decision } = true
Output: for the method records of all rows , most of them fuzzily match to decision .
eq { hop { argmax { all_rows ; tied } ; season } ; 1968 - 69 } = true
Output: select the row whose tied record of all rows is maximum . the season record of this row is 1968  69 .
eq { count { filter_eq { filter_eq { all_rows ; high rebounds ; udonis haslem } ; high assists ; dwyane wade } } ; 3 } = true
Output: select the rows whose high rebounds record fuzzily matches to udonis haslem . among these rows , select the rows whose high assists record fuzzily matches to dwyane wade . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; high points ; brad miller } } ; 2 } = true
Output: select the rows whose high points record fuzzily matches to brad miller . the number of such rows is 2 .
eq { count { filter_all { all_rows ; party } } ; 6 } = true
Output: select the rows whose party record is arbitrary . the number of such rows is 6 .
eq { hop { nth_argmax { all_rows ; viewers ( in millions ) ; 3 } ; season } ; 3 } = true
Output: select the row whose viewers  in millions  record of all rows is 3rd maximum . the season record of this row is 3 .
eq { hop { nth_argmin { all_rows ; build date ; 3 } ; loco name } ; hecate } = true
Output: select the row whose build date record of all rows is 3rd minimum . the loco name record of this row is hecate .
and { only { filter_eq { all_rows ; race ; super g } } ; eq { hop { filter_eq { all_rows ; race ; super g } ; season } ; 2009 } } = true
Output: select the rows whose race record fuzzily matches to super g . there is only one such row in the table . the season record of this unqiue row is 2009 .
eq { count { filter_eq { all_rows ; 2008 club ; henan } } ; 2 } = true
Output: select the rows whose 2008 club record fuzzily matches to henan . the number of such rows is 2 .
most_eq { all_rows ; first elected ; 1948 } = true
Output: for the first elected records of all rows , most of them are equal to 1948 .
eq { count { filter_eq { all_rows ; opponent ; detroit red wings } } ; 3 } = true
Output: select the rows whose opponent record fuzzily matches to detroit red wings . the number of such rows is 3 .
less { hop { filter_eq { all_rows ; country ; portugal } ; rank } ; hop { filter_eq { all_rows ; country ; cuba } ; rank } } = true
Output: select the rows whose country record fuzzily matches to portugal . take the rank record of this row . select the rows whose country record fuzzily matches to cuba . take the rank record of this row . the first record is less than the second record .
eq { diff { hop { filter_eq { all_rows ; venue ; punt road oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; kardinia park } ; crowd } } ; -4000 } = 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 kardinia park . take the crowd record of this row . the second record is 4000 larger than the first record .
less { hop { filter_eq { all_rows ; opponent ; peterborough united } ; date } ; hop { filter_eq { all_rows ; opponent ; villarreal } ; date } } = true
Output: select the rows whose opponent record fuzzily matches to peterborough united . take the date record of this row . select the rows whose opponent record fuzzily matches to villarreal . take the date record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; total } ; name } ; giuseppe meazza } = true
Output: select the row whose total record of all rows is maximum . the name record of this row is giuseppe meazza .
round_eq { avg { filter_eq { all_rows ; date ; november } ; attendance } ; 57973 } = true
Output: select the rows whose date record fuzzily matches to november . the average of the attendance record of these rows is 57973 .
eq { count { filter_eq { all_rows ; venue ; west germany } } ; 2 } = true
Output: select the rows whose venue record fuzzily matches to west germany . the number of such rows is 2 .
eq { count { filter_all { all_rows ; nation } } ; 11 } = true
Output: select the rows whose nation record is arbitrary . the number of such rows is 11 .
and { only { filter_eq { all_rows ; club ; orlando metro } } ; eq { hop { filter_eq { all_rows ; club ; orlando metro } ; name } ; olivia courtney } } = true
Output: select the rows whose club record fuzzily matches to orlando metro . there is only one such row in the table . the name record of this unqiue row is olivia courtney .
round_eq { avg { all_rows ; apparent magnitude } ; 12.9 } = true
Output: the average of the apparent magnitude record of all rows is 12.9 .
eq { hop { nth_argmax { all_rows ; earnings ; 2 } ; player } ; tom watson } = true
Output: select the row whose earnings record of all rows is 2nd maximum . the player record of this row is tom watson .
eq { hop { argmax { all_rows ; episode } ; segment c } ; goat cheese } = true
Output: select the row whose episode record of all rows is maximum . the segment c record of this row is goat cheese .
and { only { filter_eq { filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) } ; city of license / market ; pittsburgh } } ; eq { hop { filter_eq { filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) } ; city of license / market ; pittsburgh } ; station } ; kdka - tv } } = true
Output: select the rows whose current affiliation record fuzzily matches to cbs owned  and  operated  o  o  . among these rows , select the rows whose city of license  market record fuzzily matches to pittsburgh . there is only one such row in the table . the station record of this unqiue row is kdka  tv .
greater { hop { filter_eq { all_rows ; player ; chi chi rodriguez } ; wins } ; hop { filter_eq { all_rows ; player ; dave stockton } ; wins } } = true
Output: select the rows whose player record fuzzily matches to chi chi rodriguez . take the wins record of this row . select the rows whose player record fuzzily matches to dave stockton . take the wins record of this row . the first record is greater than the second record .
most_eq { all_rows ; position ; guard } = true
Output: for the position records of all rows , most of them fuzzily match to guard .
round_eq { avg { all_rows ; number of powiats } ; 2.5 } = true
Output: the average of the number of powiats record of all rows is 2.5 .
most_eq { all_rows ; category ; best actress - musical or comedy } = true
Output: for the category records of all rows , most of them fuzzily match to best actress  musical or comedy .
most_eq { all_rows ; high points ; eric gordon } = true
Output: for the high points records of all rows , most of them fuzzily match to eric gordon .
less { hop { filter_eq { all_rows ; incumbent ; leon h gavin } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; james f lind } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to leon h gavin . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to james f lind . take the first elected record of this row . the first record is less than the second record .
eq { diff { hop { filter_eq { all_rows ; incumbent ; bruce vento } ; opponent } ; hop { filter_eq { all_rows ; incumbent ; gerry sikorski } ; opponent } } ; .10 % } = true
Output: select the rows whose incumbent record fuzzily matches to bruce vento . take the opponent record of this row . select the rows whose incumbent record fuzzily matches to gerry sikorski . take the opponent record of this row . the first record is .10  larger than the second record .
eq { hop { nth_argmin { all_rows ; overall ; 4 } ; name } ; billy neighbors } = true
Output: select the row whose overall record of all rows is 4th minimum . the name record of this row is billy neighbors .
eq { count { filter_eq { filter_eq { all_rows ; location attendance ; amway arena } ; high points ; andre iguodala } } ; 2 } = true
Output: select the rows whose location attendance record fuzzily matches to amway arena . among these rows , select the rows whose high points record fuzzily matches to andre iguodala . the number of such rows is 2 .
eq { diff { hop { filter_eq { all_rows ; location ; beijing } ; year } ; hop { filter_eq { all_rows ; location ; hiroshima } ; year } } ; -4 } = true
Output: select the rows whose location record fuzzily matches to beijing . take the year record of this row . select the rows whose location record fuzzily matches to hiroshima . take the year record of this row . the second record is 4 larger than the first record .
round_eq { avg { all_rows ; area ( km square ) 2011 } ; 535.74 } = true
Output: the average of the area  km square  2011 record of all rows is 535.74 .
eq { count { filter_eq { all_rows ; gold ; 3 } } ; 4 } = true
Output: select the rows whose gold record is equal to 3 . the number of such rows is 4 .
and { only { filter_eq { all_rows ; location attendance ; great western } } ; eq { hop { filter_eq { all_rows ; location attendance ; great western } ; game } ; 35 } } = true
Output: select the rows whose location attendance record fuzzily matches to great western . there is only one such row in the table . the game record of this unqiue row is 35 .
and { only { filter_eq { all_rows ; floors ; 32 } } ; eq { hop { filter_eq { all_rows ; floors ; 32 } ; name } ; at & t building } } = true
Output: select the rows whose floors record is equal to 32 . there is only one such row in the table . the name record of this unqiue row is at  t building .
eq { hop { nth_argmax { all_rows ; quota ; 2 } ; constituency } ; dublin } = true
Output: select the row whose quota record of all rows is 2nd maximum . the constituency record of this row is dublin .
less { hop { filter_eq { all_rows ; club ; landskrona bois } ; first season of current spell } ; hop { filter_eq { all_rows ; club ; ängelholms ff } ; first season of current spell } } = true
Output: select the rows whose club record fuzzily matches to landskrona bois . take the first season of current spell record of this row . select the rows whose club record fuzzily matches to ängelholms ff . take the first season of current spell record of this row . the first record is less than the second record .
round_eq { sum { all_rows ; crowd } ; 114870 } = true
Output: the sum of the crowd record of all rows is 114870 .
most_greater { all_rows ; qual ; 150 } = true
Output: for the qual records of all rows , most of them are greater than 150 .
only { filter_eq { all_rows ; series ; gp2 series } } = true
Output: select the rows whose series record fuzzily matches to gp2 series . there is only one such row in the table .
eq { count { filter_eq { all_rows ; pos ; dnf } } ; 7 } = true
Output: select the rows whose pos record fuzzily matches to dnf . the number of such rows is 7 .
eq { hop { argmax { all_rows ; points } ; team } ; libertad } = true
Output: select the row whose points record of all rows is maximum . the team record of this row is libertad .
round_eq { sum { all_rows ; transfer fee } ; 95700000 } = true
Output: the sum of the transfer fee record of all rows is 95700000 .
round_eq { avg { all_rows ; central rate } ; 3.393 } = true
Output: the average of the central rate record of all rows is 3.393 .
eq { hop { argmin { filter_eq { all_rows ; competition ; 1994 fifa world cup qualification } ; date } ; venue } ; estadio rigoberto lópez , managua , nicaragua } = true
Output: select the rows whose competition record fuzzily matches to 1994 fifa world cup qualification . select the row whose date record of these rows is minimum . the venue record of this row is estadio rigoberto lópez , managua , nicaragua .
eq { count { filter_eq { all_rows ; classification ; absurdism } } ; 2 } = true
Output: select the rows whose classification record fuzzily matches to absurdism . the number of such rows is 2 .
eq { hop { nth_argmin { all_rows ; entered service ; 3 } ; locomotive } ; c503 } = true
Output: select the row whose entered service record of all rows is 3rd minimum . the locomotive record of this row is c503 .
eq { count { filter_eq { all_rows ; competition ; friendly } } ; 3 } = true
Output: select the rows whose competition record fuzzily matches to friendly . the number of such rows is 3 .
eq { hop { argmax { all_rows ; silver } ; nation } ; cyprus } = true
Output: select the row whose silver record of all rows is maximum . the nation record of this row is cyprus .
eq { hop { argmin { all_rows ; shot % } ; skip } ; anna kubešková } = true
Output: select the row whose shot  record of all rows is minimum . the skip record of this row is anna kubešková .
eq { hop { nth_argmin { all_rows ; finish ; 2 } ; player } ; jeff sluman } = true
Output: select the row whose finish record of all rows is 2nd minimum . the player record of this row is jeff sluman .
eq { hop { argmax { all_rows ; winning score } ; tournament } ; us senior open } = true
Output: select the row whose winning score record of all rows is maximum . the tournament record of this row is us senior open .
less { hop { filter_eq { all_rows ; competition ; 2002 african cup of nations } ; date } ; hop { filter_eq { all_rows ; competition ; 2006 fifa world cup qualification } ; date } } = true
Output: select the rows whose competition record fuzzily matches to 2002 african cup of nations . take the date record of this row . select the rows whose competition record fuzzily matches to 2006 fifa world cup qualification . take the date record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; venue ; san francisco } } ; 2 } = true
Output: select the rows whose venue record fuzzily matches to san francisco . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; attendance / g ; 2 } ; season } ; 2005 } = true
Output: select the row whose attendance  g record of all rows is 2nd maximum . the season record of this row is 2005 .
most_eq { all_rows ; event ; 50 km } = true
Output: for the event records of all rows , most of them fuzzily match to 50 km .
and { only { filter_not_eq { all_rows ; % buddhist ; - } } ; eq { hop { filter_not_eq { all_rows ; % buddhist ; - } ; area } ; ladakh } } = true
Output: select the rows whose  buddhist record is not equal to  . there is only one such row in the table . the area record of this unqiue row is ladakh .
round_eq { avg { filter_greater { all_rows ; joined ; 1981 } ; enrollment } ; 2767 } = true
Output: select the rows whose joined record is greater than 1981 . the average of the enrollment record of these rows is 2767 .
round_eq { sum { filter_eq { all_rows ; engine ; lamborghini v12 } ; points } ; 10 } = true
Output: select the rows whose engine record fuzzily matches to lamborghini v12 . the sum of the points record of these rows is 10 .
eq { count { filter_greater { all_rows ; attendance ; 30000 } } ; 3 } = true
Output: select the rows whose attendance record is greater than 30000 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; event ; 10000 m } } ; 3 } = true
Output: select the rows whose event record fuzzily matches to 10000 m . the number of such rows is 3 .
eq { hop { argmax { all_rows ; total } ; name } ; kotonishiki } = true
Output: select the row whose total record of all rows is maximum . the name record of this row is kotonishiki .
round_eq { avg { all_rows ; laps } ; 192 } = true
Output: the average of the laps record of all rows is 192 .
eq { hop { nth_argmin { all_rows ; us viewers ( millions ) ; 2 } ; title } ; sin francisco } = true
Output: select the row whose us viewers  millions  record of all rows is 2nd minimum . the title record of this row is sin francisco .
and { eq { max { all_rows ; points } ; 134 } ; eq { hop { argmax { all_rows ; points } ; year } ; 1997 } } = true
Output: the maximum points record of all rows is 134 . the year record of the row with superlative points record is 1997 .
eq { count { filter_greater { all_rows ; others ; 6000 } } ; 2 } = true
Output: select the rows whose others record is greater than 6000 . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; population ; 3 } ; ethnic group } ; saho } = true
Output: select the row whose population record of all rows is 3rd maximum . the ethnic group record of this row is saho .
and { only { filter_greater { all_rows ; bronze ; 0 } } ; eq { hop { filter_greater { all_rows ; bronze ; 0 } ; nation } ; austria } } = true
Output: select the rows whose bronze record is greater than 0 . there is only one such row in the table . the nation record of this unqiue row is austria .
and { only { filter_eq { all_rows ; ranking ; 33rd } } ; eq { hop { filter_eq { all_rows ; ranking ; 33rd } ; season } ; 1 } } = true
Output: select the rows whose ranking record fuzzily matches to 33rd . there is only one such row in the table . the season record of this unqiue row is 1 .
eq { count { filter_greater { all_rows ; assets ( us billion ) ; 100 billion } } ; 2 } = true
Output: select the rows whose assets  us billion  record is greater than 100 billion . the number of such rows is 2 .
round_eq { sum { all_rows ; enrollment } ; 8,600 } = true
Output: the sum of the enrollment record of all rows is 8,600 .
all_eq { filter_eq { all_rows ; date ; october } ; result ; l } = true
Output: select the rows whose date record fuzzily matches to october . for the result records of these rows , all of them fuzzily match to l .
round_eq { avg { all_rows ; score } ; 317.8 } = true
Output: the average of the score record of all rows is 317.8 .
all_eq { all_rows ; games played ; 36 } = true
Output: for the games played records of all rows , all of them are equal to 36 .
and { only { filter_eq { all_rows ; transcription ; makarakhom , mokkarakhom } } ; eq { hop { filter_eq { all_rows ; transcription ; makarakhom , mokkarakhom } ; english name } ; january } } = true
Output: select the rows whose transcription record fuzzily matches to makarakhom , mokkarakhom . there is only one such row in the table . the english name record of this unqiue row is january .
round_eq { avg { all_rows ; yards } ; 888.8 } = true
Output: the average of the yards record of all rows is 888.8 .
all_eq { all_rows ; party ; democratic } = true
Output: for the party records of all rows , all of them fuzzily match to democratic .
greater { hop { filter_eq { all_rows ; name ; ryan lamb } ; tries } ; hop { filter_eq { all_rows ; name ; shane drahm } ; tries } } = true
Output: select the rows whose name record fuzzily matches to ryan lamb . take the tries record of this row . select the rows whose name record fuzzily matches to shane drahm . take the tries record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; entries ; 1 } ; driver } ; michael schumacher } = true
Output: select the row whose entries record of all rows is 1st maximum . the driver record of this row is michael schumacher .
round_eq { avg { all_rows ; horwood } ; 8 } = true
Output: the average of the horwood record of all rows is 8 .
round_eq { sum { all_rows ; laps } ; 535 } = true
Output: the sum of the laps record of all rows is 535 .
eq { hop { nth_argmin { all_rows ; census ranking ; 2 } ; official name } ; dieppe } = true
Output: select the row whose census ranking record of all rows is 2nd minimum . the official name record of this row is dieppe .
round_eq { sum { all_rows ; money } ; 1497 } = true
Output: the sum of the money record of all rows is 1497 .
and { only { filter_eq { all_rows ; notes ; original release } } ; eq { hop { filter_eq { all_rows ; notes ; original release } ; date } ; 19 march 2008 } } = true
Output: select the rows whose notes record fuzzily matches to original release . there is only one such row in the table . the date record of this unqiue row is 19 march 2008 .
and { only { filter_eq { all_rows ; points ; 4 } } ; eq { hop { filter_eq { all_rows ; points ; 4 } ; year } ; 1980 } } = true
Output: select the rows whose points record is equal to 4 . there is only one such row in the table . the year record of this unqiue row is 1980 .
most_eq { all_rows ; candidates ; unopposed } = true
Output: for the candidates records of all rows , most of them fuzzily match to unopposed .
most_greater { all_rows ; enrollment ; 1000 } = true
Output: for the enrollment records of all rows , most of them are greater than 1000 .
most_eq { all_rows ; storyteller ; tucker } = true
Output: for the storyteller records of all rows , most of them fuzzily match to tucker .
eq { count { filter_greater { filter_eq { all_rows ; country ; united states } ; total ; 153 } } ; 2 } = true
Output: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is greater than 153 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; competition ; friendly } } ; 2 } = true
Output: select the rows whose competition record fuzzily matches to friendly . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; year ; 3 } ; version } ; album version } = true
Output: select the row whose year record of all rows is 3rd maximum . the version record of this row is album version .
and { only { filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 } } ; eq { hop { filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 } ; institution } ; georgia tech } } = true
Output: select the rows whose school type record fuzzily matches to public . among these rows , select the rows whose joined acc record fuzzily matches to 197 . there is only one such row in the table . the institution record of this unqiue row is georgia tech .
eq { hop { argmax { all_rows ; points } ; year } ; 1984 } = true
Output: select the row whose points record of all rows is maximum . the year record of this row is 1984 .
and { only { filter_eq { all_rows ; royal house ; jiang } } ; eq { hop { filter_eq { all_rows ; royal house ; jiang } ; state } ; qi } } = true
Output: select the rows whose royal house record fuzzily matches to jiang . there is only one such row in the table . the state record of this unqiue row is qi .
most_eq { all_rows ; high rebounds ; tim duncan } = true
Output: for the high rebounds records of all rows , most of them fuzzily match to tim duncan .
eq { hop { argmax { all_rows ; floors } ; name } ; kingdom tower } = true
Output: select the row whose floors record of all rows is maximum . the name record of this row is kingdom tower .
less { hop { filter_eq { all_rows ; name ; sue } ; discovered } ; hop { filter_eq { all_rows ; name ; bucky } ; discovered } } = true
Output: select the rows whose name record fuzzily matches to sue . take the discovered record of this row . select the rows whose name record fuzzily matches to bucky . take the discovered record of this row . the first record is less than the second record .
eq { hop { nth_argmin { all_rows ; 1st run ; 2 } ; name } ; donny robinson ( usa ) } = true
Output: select the row whose 1st run record of all rows is 2nd minimum . the name record of this row is donny robinson  usa  .
round_eq { avg { all_rows ; score in the final } ; 5 } = true
Output: the average of the score in the final record of all rows is 5 .
eq { max { all_rows ; days held } ; 700 } = true
Output: the maximum days held record of all rows is 700 .
and { eq { diff { hop { filter_eq { all_rows ; water park ; water country usa } ; rank } ; hop { filter_eq { all_rows ; water park ; siam water park } ; rank } } ; 2 } ; and { eq { hop { filter_eq { all_rows ; water park ; water country usa } ; rank } ; 20 } ; eq { hop { filter_eq { all_rows ; water park ; siam water park } ; rank } ; 18 } } } = true
Output: select the rows whose water park record fuzzily matches to water country usa . take the rank record of this row . select the rows whose water park record fuzzily matches to siam water park . take the rank record of this row . the first record is 2 larger than the second record . the rank record of the first row is 20 . the rank record of the second row is 18 .
and { only { filter_eq { all_rows ; platelet count ; decreased or unaffected } } ; eq { hop { filter_eq { all_rows ; platelet count ; decreased or unaffected } ; condition } ; bernard - soulier syndrome } } = true
Output: select the rows whose platelet count record fuzzily matches to decreased or unaffected . there is only one such row in the table . the condition record of this unqiue row is bernard  soulier syndrome .
eq { diff { hop { filter_eq { all_rows ; player ; yordanos abay } ; goals } ; hop { filter_eq { all_rows ; player ; fathi jabir } ; goals } } ; -15 } = true
Output: select the rows whose player record fuzzily matches to yordanos abay . take the goals record of this row . select the rows whose player record fuzzily matches to fathi jabir . take the goals record of this row . the second record is 15 larger than the first record .
and { only { filter_eq { all_rows ; result ; retired } } ; eq { hop { filter_eq { all_rows ; result ; retired } ; incumbent } ; edward boland } } = true
Output: select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is edward boland .
and { only { filter_eq { filter_greater { all_rows ; lost ; 1 } ; points ; 14 } } ; eq { hop { filter_eq { filter_greater { all_rows ; lost ; 1 } ; points ; 14 } ; team } ; skra warszawa } } = true
Output: select the rows whose lost record is greater than 1 . among these rows , select the rows whose points record is equal to 14 . there is only one such row in the table . the team record of this unqiue row is skra warszawa .
eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; adelaide 36ers } = true
Output: select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is adelaide 36ers .
and { only { filter_eq { all_rows ; cause of destruction ; still in existence } } ; eq { hop { filter_eq { all_rows ; cause of destruction ; still in existence } ; name } ; great pyramid of giza } } = true
Output: select the rows whose cause of destruction record fuzzily matches to still in existence . there is only one such row in the table . the name record of this unqiue row is great pyramid of giza .
round_eq { sum { all_rows ; no built } ; 84 } = true
Output: the sum of the no built record of all rows is 84 .
all_eq { all_rows ; country of origin ; philippines } = true
Output: for the country of origin records of all rows , all of them fuzzily match to philippines .
round_eq { sum { all_rows ; fatalities } ; 899 } = true
Output: the sum of the fatalities record of all rows is 899 .
eq { hop { nth_argmax { all_rows ; points ; 1 } ; name } ; jakim donaldson } = true
Output: select the row whose points record of all rows is 1st maximum . the name record of this row is jakim donaldson .
eq { count { filter_eq { all_rows ; position ; quarterback } } ; 2 } = true
Output: select the rows whose position record fuzzily matches to quarterback . the number of such rows is 2 .
round_eq { avg { all_rows ; crowd } ; 14208 } = true
Output: the average of the crowd record of all rows is 14208 .
eq { count { filter_eq { all_rows ; opponent ; amsterdam admirals } } ; 1 } = true
Output: select the rows whose opponent record fuzzily matches to amsterdam admirals . the number of such rows is 1 .
most_eq { all_rows ; class ; cat a } = true
Output: for the class records of all rows , most of them fuzzily match to cat a .
most_eq { all_rows ; partnering ; maria joao koehler } = true
Output: for the partnering records of all rows , most of them fuzzily match to maria joao koehler .
eq { count { filter_eq { all_rows ; manufacturer ; pontiac } } ; 2 } = true
Output: select the rows whose manufacturer record fuzzily matches to pontiac . the number of such rows is 2 .
most_eq { all_rows ; res ; win } = true
Output: for the res records of all rows , most of them fuzzily match to win .
and { only { filter_greater { filter_eq { all_rows ; team ; ford performance racing } ; points ; 1400 } } ; eq { hop { filter_greater { filter_eq { all_rows ; team ; ford performance racing } ; points ; 1400 } ; name } ; mark winterbottom } } = true
Output: select the rows whose team record fuzzily matches to ford performance racing . among these rows , select the rows whose points record is greater than 1400 . there is only one such row in the table . the name record of this unqiue row is mark winterbottom .
eq { count { filter_eq { all_rows ; laps ; 80 } } ; 2 } = true
Output: select the rows whose laps record is equal to 80 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; country ; great britain } } ; eq { hop { filter_eq { all_rows ; country ; great britain } ; athlete } ; emily freeman } } = true
Output: select the rows whose country record fuzzily matches to great britain . there is only one such row in the table . the athlete record of this unqiue row is emily freeman .
eq { count { filter_eq { all_rows ; country ; united states } } ; 12 } = true
Output: select the rows whose country record fuzzily matches to united states . the number of such rows is 12 .
greater { hop { filter_eq { all_rows ; year ; 1987 } ; points } ; hop { filter_eq { all_rows ; year ; 1980 } ; points } } = true
Output: select the rows whose year record fuzzily matches to 1987 . take the points record of this row . select the rows whose year record fuzzily matches to 1980 . take the points record of this row . the first record is greater than the second record .
eq { hop { argmin { all_rows ; year } ; winner } ; deep gold } = true
Output: select the row whose year record of all rows is minimum . the winner record of this row is deep gold .
round_eq { avg { all_rows ; first elected } ; 1996 } = true
Output: the average of the first elected record of all rows is 1996 .
eq { count { filter_greater { filter_eq { all_rows ; result ; w } ; attendance ; 40000 } } ; 4 } = true
Output: select the rows whose result record fuzzily matches to w . among these rows , select the rows whose attendance record is greater than 40000 . the number of such rows is 4 .
and { only { filter_eq { filter_less { all_rows ; time ; 1:40 } ; country ; canada } } ; eq { hop { filter_eq { filter_less { all_rows ; time ; 1:40 } ; country ; canada } ; athletes } ; adam van koeverden } } = true
Output: select the rows whose time record is less than 1:40 . among these rows , select the rows whose country record fuzzily matches to canada . there is only one such row in the table . the athletes record of this unqiue row is adam van koeverden .
eq { count { filter_eq { all_rows ; winning driver ; jean behra } } ; 4 } = true
Output: select the rows whose winning driver record fuzzily matches to jean behra . the number of such rows is 4 .
round_eq { avg { all_rows ; to par } ; 15.6 } = true
Output: the average of the to par record of all rows is 15.6 .
most_greater_eq { all_rows ; established ; 2000 } = true
Output: for the established records of all rows , most of them are greater than or equal to 2000 .
eq { hop { nth_argmin { filter_eq { all_rows ; college ; mercer } ; pick ; 1 } ; player } ; sam mitchell } = true
Output: select the rows whose college record fuzzily matches to mercer . select the row whose pick record of these rows is 1st minimum . the player record of this row is sam mitchell .
eq { hop { argmax { all_rows ; total } ; year } ; 2010 } = true
Output: select the row whose total record of all rows is maximum . the year record of this row is 2010 .
greater { hop { filter_eq { all_rows ; opponent ; debi purcell } ; round } ; hop { filter_eq { all_rows ; opponent ; tomomi sunaba } ; round } } = true
Output: select the rows whose opponent record fuzzily matches to debi purcell . take the round record of this row . select the rows whose opponent record fuzzily matches to tomomi sunaba . take the round record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; rebounds } ; 269 } = true
Output: the average of the rebounds record of all rows is 269 .
eq { hop { argmin { filter_eq { all_rows ; championship ; wimbledon } ; year } ; opponent in the final } ; wilhelm bungert } = true
Output: select the rows whose championship record fuzzily matches to wimbledon . select the row whose year record of these rows is minimum . the opponent in the final record of this row is wilhelm bungert .
and { only { filter_eq { all_rows ; location ; argentina } } ; eq { hop { filter_eq { all_rows ; location ; argentina } ; discipline } ; downhill } } = true
Output: select the rows whose location record fuzzily matches to argentina . there is only one such row in the table . the discipline record of this unqiue row is downhill .
eq { count { filter_less { all_rows ; goal difference ; 0 } } ; 5 } = true
Output: select the rows whose goal difference record is less than 0 . the number of such rows is 5 .
eq { hop { argmax { all_rows ; mccain % } ; county } ; eureka } = true
Output: select the row whose mccain  record of all rows is maximum . the county record of this row is eureka .
round_eq { avg { all_rows ; driver - matched db } ; 0.07 } = true
Output: the average of the driver  matched db record of all rows is 0.07 .
eq { hop { nth_argmax { all_rows ; windows ; 2 } ; source } ; international data corporation } = true
Output: select the row whose windows record of all rows is 2nd maximum . the source record of this row is international data corporation .
eq { count { filter_eq { all_rows ; director ; brian kelly } } ; 3 } = true
Output: select the rows whose director record fuzzily matches to brian kelly . the number of such rows is 3 .
round_eq { avg { all_rows ; candidates } ; 13 } = true
Output: the average of the candidates record of all rows is 13 .
greater { hop { filter_eq { all_rows ; opponent ; cleveland browns } ; attendance } ; hop { filter_eq { all_rows ; opponent ; pittsburgh steelers } ; attendance } } = true
Output: select the rows whose opponent record fuzzily matches to cleveland browns . take the attendance record of this row . select the rows whose opponent record fuzzily matches to pittsburgh steelers . take the attendance record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; directed by ; ch greenblatt & william reiss } } ; 7 } = true
Output: select the rows whose directed by record fuzzily matches to ch greenblatt  william reiss . the number of such rows is 7 .
and { eq { max { filter_eq { all_rows ; l3 cache ; 8 mb } ; frequency } ; 3 ghz } ; eq { hop { argmax { filter_eq { all_rows ; l3 cache ; 8 mb } ; frequency } ; model number } ; core i7 - 3940xm } } = true
Output: select the rows whose l3 cache record fuzzily matches to 8 mb . the maximum frequency record of these rows is 3 ghz . the model number record of the row with superlative frequency record is core i7  3940xm .
most_less { all_rows ; founded ; 1900 } = true
Output: for the founded records of all rows , most of them are less than 1900 .
most_eq { all_rows ; result ; nominated } = true
Output: for the result records of all rows , most of them fuzzily match to nominated .
eq { hop { argmax { all_rows ; spent per vote ( php ) } ; candidate ( party ) } ; jamby madrigal ( i ) } = true
Output: select the row whose spent per vote  php  record of all rows is maximum . the candidate  party  record of this row is jamby madrigal  i  .
eq { hop { nth_argmax { all_rows ; starts ; 2 } ; year } ; 1994 } = true
Output: select the row whose starts record of all rows is 2nd maximum . the year record of this row is 1994 .
and { eq { nth_min { filter_eq { all_rows ; venue ; municipal stadium , poznań } ; date ; 3 } ; 14 june 2012 } ; eq { hop { nth_argmin { filter_eq { all_rows ; venue ; municipal stadium , poznań } ; date ; 3 } ; date } ; 14 june 2012 } } = true
Output: select the rows whose venue record fuzzily matches to municipal stadium , poznań . the 3rd minimum date record of these rows is 14 june 2012 . the date record of the row with 3rd minimum date record is 14 june 2012 .
eq { count { filter_eq { all_rows ; round ; group h } } ; 3 } = true
Output: select the rows whose round record fuzzily matches to group h . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; date ; 1989 } } ; 4 } = true
Output: select the rows whose date record fuzzily matches to 1989 . the number of such rows is 4 .
eq { count { filter_all { all_rows ; wind farm } } ; 17 } = true
Output: select the rows whose wind farm record is arbitrary . the number of such rows is 17 .
and { only { filter_eq { all_rows ; country ; south africa } } ; eq { hop { filter_eq { all_rows ; country ; south africa } ; player } ; wayne westner } } = true
Output: select the rows whose country record fuzzily matches to south africa . there is only one such row in the table . the player record of this unqiue row is wayne westner .
eq { count { filter_eq { all_rows ; game site ; bears stadium } } ; 6 } = true
Output: select the rows whose game site record fuzzily matches to bears stadium . the number of such rows is 6 .
round_eq { avg { all_rows ; other details } ; 2250 } = true
Output: the average of the other details record of all rows is 2250 .
only { filter_eq { all_rows ; director ; j clark mathis } } = true
Output: select the rows whose director record fuzzily matches to j clark mathis . there is only one such row in the table .
eq { hop { nth_argmin { all_rows ; pick ; 6 } ; player } ; mike wacker } = true
Output: select the row whose pick record of all rows is 6th minimum . the player record of this row is mike wacker .
and { only { filter_eq { all_rows ; year ; 2011 } } ; eq { hop { filter_eq { all_rows ; year ; 2011 } ; song title } ; nee maatalo } } = true
Output: select the rows whose year record is equal to 2011 . there is only one such row in the table . the song title record of this unqiue row is nee maatalo .
greater { hop { filter_eq { all_rows ; warship ; chacabuco } ; speed ( knots ) } ; hop { filter_eq { all_rows ; warship ; abtao } ; speed ( knots ) } } = true
Output: select the rows whose warship record fuzzily matches to chacabuco . take the speed  knots  record of this row . select the rows whose warship record fuzzily matches to abtao . take the speed  knots  record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; length } ; name } ; 6.5 x 68 } = true
Output: select the row whose length record of all rows is maximum . the name record of this row is 6.5 x 68 .
round_eq { sum { all_rows ; total g } ; 44 } = true
Output: the sum of the total g record of all rows is 44 .
eq { count { filter_all { all_rows ; venue } } ; 6 } = true
Output: select the rows whose venue record is arbitrary . the number of such rows is 6 .
eq { hop { argmax { all_rows ; event } ; athlete } ; yousef soltani } = true
Output: select the row whose event record of all rows is maximum . the athlete record of this row is yousef soltani .
eq { count { filter_eq { filter_eq { all_rows ; launched ; 1973 } ; destination ; mars } } ; 3 } = true
Output: select the rows whose launched record fuzzily matches to 1973 . among these rows , select the rows whose destination record fuzzily matches to mars . the number of such rows is 3 .
eq { count { filter_eq { filter_eq { all_rows ; years in assembly ; 2008 } ; residence ; san francisco } } ; 2 } = true
Output: select the rows whose years in assembly record fuzzily matches to 2008 . among these rows , select the rows whose residence record fuzzily matches to san francisco . the number of such rows is 2 .
and { only { filter_eq { all_rows ; hpfs ; partial } } ; eq { hop { filter_eq { all_rows ; hpfs ; partial } ; ntfs } ; yes v1 .2 } } = true
Output: select the rows whose hpfs record fuzzily matches to partial . there is only one such row in the table . the ntfs record of this unqiue row is yes v1 .2 .