niryuu/sni-each-converted huggingface dataset:
and { only { filter_eq { all_rows ; opponent in the final ; graeme dott } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; graeme dott } ; championship } ; world series - berlin event } } = true
Output: select the rows whose opponent in the final record fuzzily matches to graeme dott . there is only one such row in the table . the championship record of this unqiue row is world series  berlin event .
eq { hop { argmax { all_rows ; rounds } ; team } ; henderson motorsports } = true
Output: select the row whose rounds record of all rows is maximum . the team record of this row is henderson motorsports .
most_eq { all_rows ; crystal structure ; tetragonal } = true
Output: for the crystal structure records of all rows , most of them fuzzily match to tetragonal .
and { only { filter_eq { all_rows ; date ; february } } ; eq { hop { filter_eq { all_rows ; date ; february } ; event } ; indianapolis boat , sport & travel show } } = true
Output: select the rows whose date record fuzzily matches to february . there is only one such row in the table . the event record of this unqiue row is indianapolis boat , sport  travel show .
greater { hop { filter_eq { all_rows ; skipper ; andy forbes } ; combined elapsed time } ; hop { filter_eq { all_rows ; skipper ; stuart jackson } ; combined elapsed time } } = true
Output: select the rows whose skipper record fuzzily matches to andy forbes . take the combined elapsed time record of this row . select the rows whose skipper record fuzzily matches to stuart jackson . take the combined elapsed time record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; away team score } ; 8.75 } = true
Output: the average of the away team score record of all rows is 8.75 .
eq { hop { argmax { all_rows ; last match } ; competition } ; uefa champions league } = true
Output: select the row whose last match record of all rows is maximum . the competition record of this row is uefa champions league .
eq { hop { nth_argmin { all_rows ; duration ; 2 } ; actor } ; nina soldano } = true
Output: select the row whose duration record of all rows is 2nd minimum . the actor record of this row is nina soldano .
eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; buffalo sabres } ; game ; 2 } ; november } ; 22 } = true
Output: select the rows whose opponent record fuzzily matches to buffalo sabres . select the row whose game record of these rows is 2nd minimum . the november record of this row is 22 .
most_eq { all_rows ; category ; utilities } = true
Output: for the category records of all rows , most of them fuzzily match to utilities .
eq { hop { argmax { all_rows ; crowd } ; venue } ; moorabbin oval } = true
Output: select the row whose crowd record of all rows is maximum . the venue record of this row is moorabbin oval .
most_greater { all_rows ; attendance ; 50,000 } = true
Output: for the attendance records of all rows , most of them are greater than 50,000 .
most_eq { all_rows ; result ; safe } = true
Output: for the result records of all rows , most of them fuzzily match to safe .
and { only { filter_greater { all_rows ; attendance ; 59000 } } ; eq { hop { filter_greater { all_rows ; attendance ; 59000 } ; date } ; october 30 , 1977 } } = true
Output: select the rows whose attendance record is greater than 59000 . there is only one such row in the table . the date record of this unqiue row is october 30 , 1977 .
most_eq { all_rows ; original release ; 196 } = true
Output: for the original release records of all rows , most of them fuzzily match to 196 .
and { only { filter_greater { all_rows ; wins ; 0 } } ; eq { hop { filter_greater { all_rows ; wins ; 0 } ; tournament } ; the open championship } } = true
Output: select the rows whose wins record is greater than 0 . there is only one such row in the table . the tournament record of this unqiue row is the open championship .
greater { hop { filter_eq { all_rows ; incumbent ; edward everett eslick } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; gordon browning } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to edward everett eslick . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to gordon browning . take the first elected record of this row . the first record is greater than the second record .
most_less { all_rows ; capacity ; 10000 } = true
Output: for the capacity records of all rows , most of them are less than 10000 .
most_eq { all_rows ; day / boarding ; day & boarding } = true
Output: for the day  boarding records of all rows , most of them fuzzily match to day  boarding .
all_eq { all_rows ; original air date ; 2011 } = true
Output: for the original air date records of all rows , all of them fuzzily match to 2011 .
less { hop { filter_eq { all_rows ; host ; jack arute } ; year } ; hop { filter_eq { all_rows ; host ; allen bestwick } ; year } } = true
Output: select the rows whose host record fuzzily matches to jack arute . take the year record of this row . select the rows whose host record fuzzily matches to allen bestwick . take the year record of this row . the first record is less than the second record .
most_eq { all_rows ; cancelable ; no } = true
Output: for the cancelable records of all rows , most of them fuzzily match to no .
eq { hop { argmax { all_rows ; quantity } ; class } ; 9c & 9f } = true
Output: select the row whose quantity record of all rows is maximum . the class record of this row is 9c  9f .
less { hop { filter_eq { all_rows ; club ; glais rfc } ; lost } ; hop { filter_eq { all_rows ; club ; tycroes rfc } ; lost } } = true
Output: select the rows whose club record fuzzily matches to glais rfc . take the lost record of this row . select the rows whose club record fuzzily matches to tycroes rfc . take the lost record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; name ; uchana kalan } ; number of electorates ( 2009 ) } ; hop { filter_eq { all_rows ; name ; bawani khera } ; number of electorates ( 2009 ) } } = true
Output: select the rows whose name record fuzzily matches to uchana kalan . take the number of electorates  2009  record of this row . select the rows whose name record fuzzily matches to bawani khera . take the number of electorates  2009  record of this row . the first record is greater than the second record .
most_greater { all_rows ; position ; 10 } = true
Output: for the position records of all rows , most of them are greater than 10 .
eq { hop { nth_argmax { all_rows ; event ; 2 } ; athlete } ; mostafa abdollahi } = true
Output: select the row whose event record of all rows is 2nd maximum . the athlete record of this row is mostafa abdollahi .
and { only { filter_eq { all_rows ; chassis ; lesovsky } } ; eq { hop { filter_eq { all_rows ; chassis ; lesovsky } ; year } ; 1952 } } = true
Output: select the rows whose chassis record fuzzily matches to lesovsky . there is only one such row in the table . the year record of this unqiue row is 1952 .
and { only { filter_eq { all_rows ; wins ; 10 } } ; eq { hop { filter_eq { all_rows ; wins ; 10 } ; club } ; east bengal club } } = true
Output: select the rows whose wins record is equal to 10 . there is only one such row in the table . the club record of this unqiue row is east bengal club .
and { only { filter_eq { all_rows ; school / club team ; eastern washington } } ; eq { hop { filter_eq { all_rows ; school / club team ; eastern washington } ; player } ; henry bekkering } } = true
Output: select the rows whose school  club team record fuzzily matches to eastern washington . there is only one such row in the table . the player record of this unqiue row is henry bekkering .
less { hop { filter_eq { all_rows ; opponent ; green bay packers } ; date } ; hop { filter_eq { all_rows ; opponent ; baltimore colts } ; date } } = true
Output: select the rows whose opponent record fuzzily matches to green bay packers . take the date record of this row . select the rows whose opponent record fuzzily matches to baltimore colts . take the date record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; frequency mhz } ; 400.3 } = true
Output: the average of the frequency mhz record of all rows is 400.3 .
eq { hop { argmax { all_rows ; attendance } ; week } ; 14 } = true
Output: select the row whose attendance record of all rows is maximum . the week record of this row is 14 .
eq { count { filter_eq { filter_eq { all_rows ; home ; ottawa } ; decision ; hasek } } ; 4 } = true
Output: select the rows whose home record fuzzily matches to ottawa . among these rows , select the rows whose decision record fuzzily matches to hasek . the number of such rows is 4 .
eq { hop { nth_argmax { all_rows ; speed ; 3 } ; rider } ; ray pickrell } = true
Output: select the row whose speed record of all rows is 3rd maximum . the rider record of this row is ray pickrell .
and { only { filter_less { all_rows ; tournaments played ; 20 } } ; eq { hop { filter_less { all_rows ; tournaments played ; 20 } ; year } ; 2002 } } = true
Output: select the rows whose tournaments played record is less than 20 . there is only one such row in the table . the year record of this unqiue row is 2002 .
less { hop { filter_eq { all_rows ; model / engine ; 1.8 duratec he } ; capacity } ; hop { filter_eq { all_rows ; model / engine ; 2.0 duratec he } ; capacity } } = true
Output: select the rows whose model  engine record fuzzily matches to 1.8 duratec he . take the capacity record of this row . select the rows whose model  engine record fuzzily matches to 2.0 duratec he . take the capacity record of this row . the first record is less than the second record .
eq { count { filter_eq { filter_eq { all_rows ; label ; atlantic records } ; region ; united states } } ; 4 } = true
Output: select the rows whose label record fuzzily matches to atlantic records . among these rows , select the rows whose region record fuzzily matches to united states . the number of such rows is 4 .
most_eq { all_rows ; competition ; world cross country championships } = true
Output: for the competition records of all rows , most of them fuzzily match to world cross country championships .
round_eq { sum { all_rows ; hotel rooms } ; 18447 } = true
Output: the sum of the hotel rooms record of all rows is 18447 .
most_greater { all_rows ; attendance ; 40000 } = true
Output: for the attendance records of all rows , most of them are greater than 40000 .
round_eq { sum { all_rows ; total } ; 119 } = true
Output: the sum of the total record of all rows is 119 .
round_eq { avg { all_rows ; episodes } ; 38.167 } = true
Output: the average of the episodes record of all rows is 38.167 .
most_eq { all_rows ; stadium ; edison international field } = true
Output: for the stadium records of all rows , most of them fuzzily match to edison international field .
and { eq { nth_min { all_rows ; round ; 3 } ; 3 } ; eq { hop { nth_argmin { all_rows ; round ; 3 } ; name } ; scott starks } } = true
Output: the 3rd minimum round record of all rows is 3 . the name record of the row with 3rd minimum round record is scott starks .
eq { hop { nth_argmax { all_rows ; height ( m ) ; 2 } ; island } ; ailsa craig } = true
Output: select the row whose height  m  record of all rows is 2nd maximum . the island record of this row is ailsa craig .
eq { hop { argmin { all_rows ; against } ; opposing teams } ; ireland } = true
Output: select the row whose against record of all rows is minimum . the opposing teams record of this row is ireland .
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; home team } ; milton keynes dons } = true
Output: select the row whose attendance record of all rows is 2nd maximum . the home team record of this row is milton keynes dons .
and { only { filter_less { all_rows ; gdp per capita ( us ) ; 16000 } } ; eq { hop { filter_less { all_rows ; gdp per capita ( us ) ; 16000 } ; member countries } ; finland } } = true
Output: select the rows whose gdp per capita  us  record is less than 16000 . there is only one such row in the table . the member countries record of this unqiue row is finland .
eq { nth_max { all_rows ; date ; 3 } ; september 18 , 2005 } = true
Output: the 3rd maximum date record of all rows is september 18 , 2005 .
and { eq { diff { hop { filter_eq { all_rows ; runner up ; sydney swans } ; attendance } ; hop { filter_eq { all_rows ; runner up ; hawthorn } ; attendance } } ; -61861 } ; and { eq { hop { filter_eq { all_rows ; runner up ; sydney swans } ; attendance } ; 30824 } ; eq { hop { filter_eq { all_rows ; runner up ; hawthorn } ; attendance } ; 92685 } } } = true
Output: select the rows whose runner up record fuzzily matches to sydney swans . take the attendance record of this row . select the rows whose runner up record fuzzily matches to hawthorn . take the attendance record of this row . the second record is 61861 larger than the first record . the attendance record of the first row is 30824 . the attendance record of the second row is 92685 .
less { hop { filter_eq { all_rows ; episode title ; remember } ; original air date } ; hop { filter_eq { all_rows ; episode title ; big brotherly love } ; original air date } } = true
Output: select the rows whose episode title record fuzzily matches to remember . take the original air date record of this row . select the rows whose episode title record fuzzily matches to big brotherly love . take the original air date record of this row . the first record is less than the second record .
eq { hop { argmin { all_rows ; start date } ; minister } ; eugène schaus } = true
Output: select the row whose start date record of all rows is minimum . the minister record of this row is eugène schaus .
eq { hop { argmax { all_rows ; enrollment } ; institution } ; queens college } = true
Output: select the row whose enrollment record of all rows is maximum . the institution record of this row is queens college .
most_eq { all_rows ; method ; tko } = true
Output: for the method records of all rows , most of them fuzzily match to tko .
round_eq { avg { all_rows ; entrants } ; 399 } = true
Output: the average of the entrants record of all rows is 399 .
eq { count { filter_greater { all_rows ; crowd ; 20,000 } } ; 4 } = true
Output: select the rows whose crowd record is greater than 20,000 . the number of such rows is 4 .
round_eq { sum { all_rows ; total } ; 1,353 } = true
Output: the sum of the total record of all rows is 1,353 .
round_eq { avg { all_rows ; total points } ; 962.2 } = true
Output: the average of the total points record of all rows is 962.2 .
eq { diff { hop { filter_eq { all_rows ; player ; scott simpson } ; place } ; hop { filter_eq { all_rows ; player ; larry mize } ; place } } ; -3 } = true
Output: select the rows whose player record fuzzily matches to scott simpson . take the place record of this row . select the rows whose player record fuzzily matches to larry mize . take the place record of this row . the second record is 3 larger than the first record .
and { less { hop { filter_eq { all_rows ; archbishop ; jean baptiste lamy } ; born } ; hop { filter_eq { all_rows ; archbishop ; jean baptiste salpointe } ; born } } ; and { eq { hop { filter_eq { all_rows ; archbishop ; jean baptiste lamy } ; born } ; october 11 , 1814 } ; eq { hop { filter_eq { all_rows ; archbishop ; jean baptiste salpointe } ; born } ; february 22 , 1825 } } } = true
Output: select the rows whose archbishop record fuzzily matches to jean baptiste lamy . take the born record of this row . select the rows whose archbishop record fuzzily matches to jean baptiste salpointe . take the born record of this row . the first record is less than the second record . the born record of the first row is october 11 , 1814 . the born record of the second row is february 22 , 1825 .
greater { hop { filter_eq { all_rows ; date ; september 6 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 22 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to september 6 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 22 . take the attendance record of this row . the first record is greater than the second record .
eq { hop { argmin { all_rows ; birthdate } ; name } ; phil verchota } = true
Output: select the row whose birthdate record of all rows is minimum . the name record of this row is phil verchota .
and { greater { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } } ; and { eq { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; 1200 kw 500 kw } ; eq { hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } ; 200 kw 200 kw } } } = true
Output: select the rows whose city record fuzzily matches to newcastle . take the erp  analog  digital  record of this row . select the rows whose city record fuzzily matches to lismore . take the erp  analog  digital  record of this row . the first record is greater than the second record . the erp  analog  digital  record of the first row is 1200 kw 500 kw . the erp  analog  digital  record of the second row is 200 kw 200 kw .
eq { count { filter_eq { filter_not_eq { all_rows ; visitor ; toronto } ; points ; 28 } } ; 2 } = true
Output: select the rows whose visitor record does not match to toronto . among these rows , select the rows whose points record is equal to 28 . the number of such rows is 2 .
and { greater { hop { filter_eq { all_rows ; date ; november 18 , 1962 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 16 , 1962 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; november 18 , 1962 } ; attendance } ; 12500 } ; eq { hop { filter_eq { all_rows ; date ; december 16 , 1962 } ; attendance } ; 8000 } } } = true
Output: select the rows whose date record fuzzily matches to november 18 , 1962 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 16 , 1962 . take the attendance record of this row . the first record is greater than the second record . the attendance record of the first row is 12500 . the attendance record of the second row is 8000 .
greater { hop { filter_eq { all_rows ; local name ; det store korslaget } ; year premiered } ; hop { filter_eq { all_rows ; local name ; körslaget } ; year premiered } } = true
Output: select the rows whose local name record fuzzily matches to det store korslaget . take the year premiered record of this row . select the rows whose local name record fuzzily matches to körslaget . take the year premiered record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; to par } ; 3 } = true
Output: the average of the to par record of all rows is 3 .
and { only { filter_eq { all_rows ; finish ; 10 } } ; eq { hop { filter_eq { all_rows ; finish ; 10 } ; year } ; 1972 } } = true
Output: select the rows whose finish record is equal to 10 . there is only one such row in the table . the year record of this unqiue row is 1972 .
round_eq { avg { all_rows ; to par } ; -7 } = true
Output: the average of the to par record of all rows is 7 .
round_eq { avg { all_rows ; attendance } ; 21273 } = true
Output: the average of the attendance record of all rows is 21273 .
and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; date } ; 10 april 2011 } } = true
Output: select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the date record of this unqiue row is 10 april 2011 .
and { only { filter_eq { all_rows ; points ; 0 } } ; eq { hop { filter_eq { all_rows ; points ; 0 } ; team } ; sírio } } = true
Output: select the rows whose points record is equal to 0 . there is only one such row in the table . the team record of this unqiue row is sírio .
eq { hop { nth_argmin { all_rows ; overall ; 3 } ; name } ; brandon burlsworth } = true
Output: select the row whose overall record of all rows is 3rd minimum . the name record of this row is brandon burlsworth .
eq { hop { nth_argmax { all_rows ; seats ( acs ) ; 1 } ; state } ; madhya pradesh } = true
Output: select the row whose seats  acs  record of all rows is 1st maximum . the state record of this row is madhya pradesh .
eq { hop { argmin { all_rows ; time } ; nationality } ; zimbabwe } = true
Output: select the row whose time record of all rows is minimum . the nationality record of this row is zimbabwe .
eq { hop { nth_argmax { all_rows ; 2011 ( imf ) ; 3 } ; nation } ; uruguay } = true
Output: select the row whose 2011  imf  record of all rows is 3rd maximum . the nation record of this row is uruguay .
and { only { filter_eq { all_rows ; country ; zimbabwe } } ; eq { hop { filter_eq { all_rows ; country ; zimbabwe } ; player } ; nick price } } = true
Output: select the rows whose country record fuzzily matches to zimbabwe . there is only one such row in the table . the player record of this unqiue row is nick price .
eq { count { filter_all { all_rows ; season } } ; 6 } = true
Output: select the rows whose season record is arbitrary . the number of such rows is 6 .
eq { hop { argmin { all_rows ; elevated } ; elector } ; gregorio papareschi , canreglat } = true
Output: select the row whose elevated record of all rows is minimum . the elector record of this row is gregorio papareschi , canreglat .
less { hop { filter_eq { all_rows ; team ; eurointernational } ; season } ; hop { filter_eq { all_rows ; team ; us racetronics } ; season } } = true
Output: select the rows whose team record fuzzily matches to eurointernational . take the season record of this row . select the rows whose team record fuzzily matches to us racetronics . take the season record of this row . the first record is less than the second record .
eq { count { filter_all { all_rows ; lost } } ; 8 } = true
Output: select the rows whose lost record is arbitrary . the number of such rows is 8 .
eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; name } ; orangeville district secondary school } = true
Output: select the row whose enrollment record of all rows is 2nd maximum . the name record of this row is orangeville district secondary school .
greater { hop { filter_eq { all_rows ; school ; munster } ; enrollment } ; hop { filter_eq { all_rows ; school ; andrean } ; enrollment } } = true
Output: select the rows whose school record fuzzily matches to munster . take the enrollment record of this row . select the rows whose school record fuzzily matches to andrean . take the enrollment record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; tournament ; bath } ; date } ; hop { filter_eq { all_rows ; tournament ; hull } ; date } } = true
Output: select the rows whose tournament record fuzzily matches to bath . take the date record of this row . select the rows whose tournament record fuzzily matches to hull . take the date record of this row . the first record is less than the second record .
and { only { filter_greater { all_rows ; gross tonnage ; 4000 } } ; eq { hop { filter_greater { all_rows ; gross tonnage ; 4000 } ; type } ; general cargo ship / container ship } } = true
Output: select the rows whose gross tonnage record is greater than 4000 . there is only one such row in the table . the type record of this unqiue row is general cargo ship  container ship .
eq { count { filter_greater_eq { all_rows ; td 's ; 1 } } ; 2 } = true
Output: select the rows whose td s record is greater than or equal to 1 . the number of such rows is 2 .
eq { count { filter_eq { filter_eq { all_rows ; class ; senior } ; position ; guard } } ; 2 } = true
Output: select the rows whose class record fuzzily matches to senior . among these rows , select the rows whose position record fuzzily matches to guard . the number of such rows is 2 .
eq { count { filter_eq { filter_eq { all_rows ; junctions ; sh 359 us 59 } ; termini ; aguilares , texas us 59 } } ; 1 } = true
Output: select the rows whose junctions record fuzzily matches to sh 359 us 59 . among these rows , select the rows whose termini record fuzzily matches to aguilares , texas us 59 . the number of such rows is 1 .
eq { count { filter_not_eq { all_rows ; result ; retired } } ; 4 } = true
Output: select the rows whose result record does not match to retired . the number of such rows is 4 .
most_eq { all_rows ; issue price ; 24.95 } = true
Output: for the issue price records of all rows , most of them are equal to 24.95 .
round_eq { avg { all_rows ; points } ; 68.7 } = true
Output: the average of the points record of all rows is 68.7 .
eq { diff { hop { filter_eq { all_rows ; player ; jeff feniak } ; round } ; hop { filter_eq { all_rows ; player ; konstantin rudenko } ; round } } ; -2 } = true
Output: select the rows whose player record fuzzily matches to jeff feniak . take the round record of this row . select the rows whose player record fuzzily matches to konstantin rudenko . take the round record of this row . the second record is 2 larger than the first record .
and { only { filter_not_eq { all_rows ; engine ; 2.4 } } ; eq { hop { filter_not_eq { all_rows ; engine ; 2.4 } ; trim } ; xe ( 2007 - 08 ) } } = true
Output: select the rows whose engine record is not equal to 2.4 . there is only one such row in the table . the trim record of this unqiue row is xe  2007  08  .
eq { hop { argmax { all_rows ; runs } ; opponent } ; queensland } = true
Output: select the row whose runs record of all rows is maximum . the opponent record of this row is queensland .
eq { hop { nth_argmin { all_rows ; pick ; 1 } ; player } ; gene carr } = true
Output: select the row whose pick record of all rows is 1st minimum . the player record of this row is gene carr .
less { hop { filter_eq { all_rows ; player ; hakim akbar } ; round } ; hop { filter_eq { all_rows ; player ; leonard myers } ; round } } = true
Output: select the rows whose player record fuzzily matches to hakim akbar . take the round record of this row . select the rows whose player record fuzzily matches to leonard myers . take the round record of this row . the first record is less than the second record .
and { only { filter_greater { all_rows ; crowd ; 30000 } } ; eq { hop { filter_greater { all_rows ; crowd ; 30000 } ; home team } ; melbourne } } = true
Output: select the rows whose crowd record is greater than 30000 . there is only one such row in the table . the home team record of this unqiue row is melbourne .
and { only { filter_less { all_rows ; goals ; 55 } } ; eq { hop { filter_less { all_rows ; goals ; 55 } ; player } ; eusébio } } = true
Output: select the rows whose goals record is less than 55 . there is only one such row in the table . the player record of this unqiue row is eusébio .
and { only { filter_eq { all_rows ; nationality ; switzerland } } ; eq { hop { filter_eq { all_rows ; nationality ; switzerland } ; player } ; julien vauclair } } = true
Output: select the rows whose nationality record fuzzily matches to switzerland . there is only one such row in the table . the player record of this unqiue row is julien vauclair .
and { only { filter_eq { all_rows ; 1st round ; 0-0 } } ; and { eq { hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 1 } ; as angoulême ( d2 ) } ; eq { hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 2 } ; fc sochaux - montbéliard ( d1 ) } } } = true
Output: select the rows whose 1st round record fuzzily matches to 00 . there is only one such row in the table . the team 1 record of this unqiue row is as angoulême  d2  . the team 2 record of this unqiue row is fc sochaux  montbéliard  d1  .
eq { hop { nth_argmax { all_rows ; tournaments ; 2 } ; name } ; kyokunankai } = true
Output: select the row whose tournaments record of all rows is 2nd maximum . the name record of this row is kyokunankai .
eq { count { filter_eq { all_rows ; location ; seattle } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to seattle . the number of such rows is 3 .
round_eq { avg { all_rows ; points } ; 2.5 } = true
Output: the average of the points record of all rows is 2.5 .
eq { hop { argmax { filter_eq { all_rows ; constructor ; maserati } ; grid } ; driver } ; luigi piotti } = true
Output: select the rows whose constructor record fuzzily matches to maserati . select the row whose grid record of these rows is maximum . the driver record of this row is luigi piotti .
greater { hop { filter_eq { all_rows ; series ; formula renault 2.0 italy } ; points } ; hop { filter_eq { all_rows ; series ; italian formula three } ; points } } = true
Output: select the rows whose series record fuzzily matches to formula renault 2.0 italy . take the points record of this row . select the rows whose series record fuzzily matches to italian formula three . take the points record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; competition ; uefa europa league } } ; 4 } = true
Output: select the rows whose competition record fuzzily matches to uefa europa league . the number of such rows is 4 .
less { hop { filter_eq { all_rows ; visitor ; dallas } ; date } ; hop { filter_eq { all_rows ; visitor ; montreal } ; date } } = true
Output: select the rows whose visitor record fuzzily matches to dallas . take the date record of this row . select the rows whose visitor record fuzzily matches to montreal . take the date record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; deaths ; none } } ; eq { hop { filter_eq { all_rows ; deaths ; none } ; year } ; 1864 } } = true
Output: select the rows whose deaths record fuzzily matches to none . there is only one such row in the table . the year record of this unqiue row is 1864 .
eq { hop { nth_argmax { filter_eq { all_rows ; date ; october } ; attendance ; 1 } ; opponent } ; dallas cowboys } = true
Output: select the rows whose date record fuzzily matches to october . select the row whose attendance record of these rows is 1st maximum . the opponent record of this row is dallas cowboys .
all_eq { all_rows ; date ; 25 may 1940 } = true
Output: for the date records of all rows , all of them fuzzily match to 25 may 1940 .
eq { count { filter_eq { all_rows ; result ; ot } } ; 3 } = true
Output: select the rows whose result record fuzzily matches to ot . the number of such rows is 3 .
and { only { filter_eq { all_rows ; tournament ; mci heritage golf classic } } ; eq { hop { filter_eq { all_rows ; tournament ; mci heritage golf classic } ; date } ; 17 apr 1988 } } = true
Output: select the rows whose tournament record fuzzily matches to mci heritage golf classic . there is only one such row in the table . the date record of this unqiue row is 17 apr 1988 .
eq { count { filter_eq { all_rows ; score ; ( ot ) } } ; 2 } = true
Output: select the rows whose score record fuzzily matches to  ot  . the number of such rows is 2 .
and { only { filter_greater { all_rows ; 2002 population ; 20000 } } ; eq { hop { filter_greater { all_rows ; 2002 population ; 20000 } ; commune } ; province } } = true
Output: select the rows whose 2002 population record is greater than 20000 . there is only one such row in the table . the commune record of this unqiue row is province .
eq { hop { argmax { filter_eq { all_rows ; nationality ; united states } ; jersey number ( s ) } ; player } ; frank oleynick } = true
Output: select the rows whose nationality record fuzzily matches to united states . select the row whose jersey number  s  record of these rows is maximum . the player record of this row is frank oleynick .
and { eq { hop { argmax { all_rows ; attendance } ; date } ; october 7 , 1979 } ; eq { hop { argmax { all_rows ; attendance } ; opponent } ; chicago bears } } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is october 7 , 1979 . the opponent record of this row is chicago bears .
greater { hop { filter_eq { all_rows ; title ; mount rushmore } ; original air date } ; hop { filter_eq { all_rows ; title ; salem } ; original air date } } = true
Output: select the rows whose title record fuzzily matches to mount rushmore . take the original air date record of this row . select the rows whose title record fuzzily matches to salem . take the original air date record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; days } ; 102 } = true
Output: the average of the days record of all rows is 102 .
round_eq { sum { all_rows ; gold } ; 8 } = true
Output: the sum of the gold record of all rows is 8 .
eq { count { filter_eq { all_rows ; fri 5 june ; no time } } ; 8 } = true
Output: select the rows whose fri 5 june record fuzzily matches to no time . the number of such rows is 8 .
round_eq { sum { all_rows ; money } ; 414749 } = true
Output: the sum of the money record of all rows is 414749 .
most_eq { all_rows ; tournaments ; 12 } = true
Output: for the tournaments records of all rows , most of them are equal to 12 .
eq { hop { nth_argmin { all_rows ; no ; 2 } ; player } ; rick roberson } = true
Output: select the row whose no record of all rows is 2nd minimum . the player record of this row is rick roberson .
eq { count { filter_eq { all_rows ; opponent ; hartford whalers } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to hartford whalers . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; written by ; shonda rhimes } } ; 4 } = true
Output: select the rows whose written by record fuzzily matches to shonda rhimes . the number of such rows is 4 .
eq { hop { argmax { all_rows ; earnings } ; player } ; tiger woods } = true
Output: select the row whose earnings record of all rows is maximum . the player record of this row is tiger woods .
round_eq { sum { all_rows ; votes } ; 65680 } = true
Output: the sum of the votes record of all rows is 65680 .
eq { hop { argmax { all_rows ; runs } ; batsmen } ; graham gooch ken mcewan } = true
Output: select the row whose runs record of all rows is maximum . the batsmen record of this row is graham gooch ken mcewan .
eq { count { filter_eq { all_rows ; issue price ; 79.95 } } ; 2 } = true
Output: select the rows whose issue price record is equal to 79.95 . the number of such rows is 2 .
most_eq { all_rows ; high points ; paul westphal } = true
Output: for the high points records of all rows , most of them fuzzily match to paul westphal .
and { only { filter_eq { all_rows ; category ; 1 } } ; eq { hop { filter_eq { all_rows ; category ; 1 } ; leader at the summit } ; richard virenque } } = true
Output: select the rows whose category record is equal to 1 . there is only one such row in the table . the leader at the summit record of this unqiue row is richard virenque .
and { only { filter_eq { all_rows ; country ; sweden } } ; eq { hop { filter_eq { all_rows ; country ; sweden } ; player } ; per - ulrik johansson } } = true
Output: select the rows whose country record fuzzily matches to sweden . there is only one such row in the table . the player record of this unqiue row is per  ulrik johansson .
eq { hop { nth_argmax { all_rows ; run time ; 2 } ; episode number } ; 311 } = true
Output: select the row whose run time record of all rows is 2nd maximum . the episode number record of this row is 311 .
all_eq { all_rows ; result ; not nominated } = true
Output: for the result records of all rows , all of them fuzzily match to not nominated .
round_eq { avg { all_rows ; transit passengers } ; 44574 } = true
Output: the average of the transit passengers record of all rows is 44574 .
less { hop { filter_eq { all_rows ; venue ; kuwait } ; date } ; hop { filter_eq { all_rows ; venue ; amman } ; date } } = true
Output: select the rows whose venue record fuzzily matches to kuwait . take the date record of this row . select the rows whose venue record fuzzily matches to amman . take the date record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; city of license ; syracuse , ny } } ; eq { hop { filter_eq { all_rows ; city of license ; syracuse , ny } ; call sign } ; wmhr } } = true
Output: select the rows whose city of license record fuzzily matches to syracuse , ny . there is only one such row in the table . the call sign record of this unqiue row is wmhr .
eq { hop { argmax { all_rows ; attendance } ; date } ; october 10 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is october 10 .
round_eq { avg { all_rows ; points } ; 9.4 } = true
Output: the average of the points record of all rows is 9.4 .
eq { hop { argmax { all_rows ; population } ; english name } ; hedong district } = true
Output: select the row whose population record of all rows is maximum . the english name record of this row is hedong district .
eq { hop { argmax { all_rows ; location attendance } ; date } ; april 2 } = true
Output: select the row whose location attendance record of all rows is maximum . the date record of this row is april 2 .
eq { count { filter_eq { all_rows ; position ; rb } } ; 3 } = true
Output: select the rows whose position record fuzzily matches to rb . the number of such rows is 3 .
round_eq { avg { all_rows ; score } ; 90.5 } = true
Output: the average of the score record of all rows is 90.5 .
and { only { filter_eq { filter_eq { all_rows ; court surface ; hard } ; location ; shanghai } } ; eq { hop { filter_eq { filter_eq { all_rows ; court surface ; hard } ; location ; shanghai } ; tournament } ; shanghai masters } } = true
Output: select the rows whose court surface record fuzzily matches to hard . among these rows , select the rows whose location record fuzzily matches to shanghai . there is only one such row in the table . the tournament record of this unqiue row is shanghai masters .
eq { count { filter_all { all_rows ; game } } ; 12 } = true
Output: select the rows whose game record is arbitrary . the number of such rows is 12 .
most_eq { filter_eq { all_rows ; date ; dec } ; result ; win } = true
Output: select the rows whose date record fuzzily matches to dec . for the result records of these rows , most of them fuzzily match to win .
and { only { filter_less { all_rows ; contributions ; 5000000 } } ; eq { hop { filter_less { all_rows ; contributions ; 5000000 } ; candidate } ; dennis kucinich } } = true
Output: select the rows whose contributions record is less than 5000000 . there is only one such row in the table . the candidate record of this unqiue row is dennis kucinich .
and { only { filter_not_eq { all_rows ; bubbles ; yes } } ; eq { hop { filter_not_eq { all_rows ; bubbles ; yes } ; type } ; domnoderemovedfromdocument } } = true
Output: select the rows whose bubbles record does not match to yes . there is only one such row in the table . the type record of this unqiue row is domnoderemovedfromdocument .
round_eq { sum { all_rows ; bronze } ; 53 } = true
Output: the sum of the bronze record of all rows is 53 .
and { only { filter_eq { all_rows ; race winner ; clay regazzoni } } ; eq { hop { filter_eq { all_rows ; race winner ; clay regazzoni } ; date } ; 28 march } } = true
Output: select the rows whose race winner record fuzzily matches to clay regazzoni . there is only one such row in the table . the date record of this unqiue row is 28 march .
eq { hop { argmax { all_rows ; overall record } ; school } ; delmar } = true
Output: select the row whose overall record record of all rows is maximum . the school record of this row is delmar .
eq { hop { argmin { all_rows ; founded } ; club } ; fk vojvodina } = true
Output: select the row whose founded record of all rows is minimum . the club record of this row is fk vojvodina .
less { hop { filter_eq { all_rows ; team ; south china } ; loss } ; hop { filter_eq { all_rows ; team ; xiangxue pharmaceutical } ; loss } } = true
Output: select the rows whose team record fuzzily matches to south china . take the loss record of this row . select the rows whose team record fuzzily matches to xiangxue pharmaceutical . take the loss record of this row . the first record is less than the second record .
eq { hop { nth_argmin { all_rows ; us air date ; 3 } ; us air date } ; july 3 , 1993 } = true
Output: select the row whose us air date record of all rows is 3rd minimum . the us air date record of this row is july 3 , 1993 .
only { filter_less { all_rows ; attendance ; 10000 } } = true
Output: select the rows whose attendance record is less than 10000 . there is only one such row in the table .
eq { hop { argmax { all_rows ; date } ; championship } ; newport , rhode island , us } = true
Output: select the row whose date record of all rows is maximum . the championship record of this row is newport , rhode island , us .
round_eq { sum { filter_eq { all_rows ; manufacturer ; schenectady } ; quantity made } ; 27 } = true
Output: select the rows whose manufacturer record fuzzily matches to schenectady . the sum of the quantity made record of these rows is 27 .
greater { hop { filter_eq { all_rows ; player ; tom watson } ; total } ; hop { filter_eq { all_rows ; player ; tom weiskopf } ; total } } = true
Output: select the rows whose player record fuzzily matches to tom watson . take the total record of this row . select the rows whose player record fuzzily matches to tom weiskopf . take the total record of this row . the first record is greater than the second record .
and { only { filter_eq { filter_eq { all_rows ; captain ; a simpson } ; chairman ; j magowan } } ; eq { hop { filter_eq { filter_eq { all_rows ; captain ; a simpson } ; chairman ; j magowan } ; year } ; 2007 } } = true
Output: select the rows whose captain record fuzzily matches to a simpson . among these rows , select the rows whose chairman record fuzzily matches to j magowan . there is only one such row in the table . the year record of this unqiue row is 2007 .
and { only { filter_eq { filter_eq { all_rows ; high points ; dwight howard } ; high rebounds ; hedo türkoğlu } } ; eq { hop { filter_eq { filter_eq { all_rows ; high points ; dwight howard } ; high rebounds ; hedo türkoğlu } ; date } ; march 23 } } = true
Output: select the rows whose high points record fuzzily matches to dwight howard . among these rows , select the rows whose high rebounds record fuzzily matches to hedo türkoğlu . there is only one such row in the table . the date record of this unqiue row is march 23 .
round_eq { avg { all_rows ; blank ends } ; 6.9 } = true
Output: the average of the blank ends record of all rows is 6.9 .
eq { hop { argmax { all_rows ; games } ; player } ; kevin anderson } = true
Output: select the row whose games record of all rows is maximum . the player record of this row is kevin anderson .
round_eq { avg { all_rows ; crowd } ; 24,648 } = true
Output: the average of the crowd record of all rows is 24,648 .
eq { max { all_rows ; hydropathy index } ; 4.5 } = true
Output: the maximum hydropathy index record of all rows is 4.5 .
eq { hop { nth_argmin { all_rows ; year ; 2 } ; perpetrator } ; wagner , ernst august , 38 } = true
Output: select the row whose year record of all rows is 2nd minimum . the perpetrator record of this row is wagner , ernst august , 38 .
eq { hop { nth_argmax { all_rows ; sail area ; 2 } ; model } ; 5.8 } = true
Output: select the row whose sail area record of all rows is 2nd maximum . the model record of this row is 5.8 .
eq { hop { argmax { all_rows ; no in series } ; written by } ; jeanne leitenberg } = true
Output: select the row whose no in series record of all rows is maximum . the written by record of this row is jeanne leitenberg .
most_eq { all_rows ; high rebounds ; kris humphries } = true
Output: for the high rebounds records of all rows , most of them fuzzily match to kris humphries .
round_eq { avg { all_rows ; blocks per game } ; 2.12 } = true
Output: the average of the blocks per game record of all rows is 2.12 .
round_eq { avg { all_rows ; design flow ( lpm ) } ; 823.57 } = true
Output: the average of the design flow  lpm  record of all rows is 823.57 .
eq { diff { hop { filter_eq { all_rows ; player ; alvan adams } ; from } ; hop { filter_eq { all_rows ; player ; rafael addison } ; from } } ; -11 } = true
Output: select the rows whose player record fuzzily matches to alvan adams . take the from record of this row . select the rows whose player record fuzzily matches to rafael addison . take the from record of this row . the second record is 11 larger than the first record .
round_eq { sum { all_rows ; round } ; 8 } = true
Output: the sum of the round record of all rows is 8 .
eq { hop { argmax { all_rows ; central rate } ; currency } ; danish krone } = true
Output: select the row whose central rate record of all rows is maximum . the currency record of this row is danish krone .
and { only { filter_eq { all_rows ; position ; cornerback } } ; eq { hop { filter_eq { all_rows ; position ; cornerback } ; player } ; kevin thomas } } = true
Output: select the rows whose position record fuzzily matches to cornerback . there is only one such row in the table . the player record of this unqiue row is kevin thomas .
greater { hop { filter_eq { all_rows ; tournament ; the open championship } ; events } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; events } } = true
Output: select the rows whose tournament record fuzzily matches to the open championship . take the events record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the events record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; state ; cao } } ; eq { hop { filter_eq { all_rows ; state ; cao } ; name } ; zhong } } = true
Output: select the rows whose state record fuzzily matches to cao . there is only one such row in the table . the name record of this unqiue row is zhong .
round_eq { avg { all_rows ; year erected } ; 1965 } = true
Output: the average of the year erected record of all rows is 1965 .
and { only { filter_less { all_rows ; earnings ; 560000 } } ; eq { hop { filter_less { all_rows ; earnings ; 560000 } ; player } ; gene littler } } = true
Output: select the rows whose earnings record is less than 560000 . there is only one such row in the table . the player record of this unqiue row is gene littler .
round_eq { avg { all_rows ; score } ; 281 } = true
Output: the average of the score record of all rows is 281 .
less { hop { filter_eq { all_rows ; incumbent ; dale kildee } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; fred upton } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to dale kildee . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to fred upton . take the first elected record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; incumbent ; lane evans } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; phil crane } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to lane evans . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to phil crane . take the first elected record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; model ; 450s } ; displacement cc } ; hop { filter_eq { all_rows ; model ; 4cl } ; displacement cc } } = true
Output: select the rows whose model record fuzzily matches to 450s . take the displacement cc record of this row . select the rows whose model record fuzzily matches to 4cl . take the displacement cc record of this row . the first record is greater than the second record .
and { eq { nth_min { all_rows ; 2004 - 2005 season ; 1 } ; 1st in the liga } ; eq { hop { nth_argmin { all_rows ; 2004 - 2005 season ; 1 } ; club } ; benfica } } = true
Output: the 1st minimum 2004  2005 season record of all rows is 1st in the liga . the club record of the row with 1st minimum 2004  2005 season record is benfica .
eq { count { filter_all { all_rows ; rider } } ; 8 } = true
Output: select the rows whose rider record is arbitrary . the number of such rows is 8 .
eq { count { filter_eq { all_rows ; directed by ; michael lange } } ; 9 } = true
Output: select the rows whose directed by record fuzzily matches to michael lange . the number of such rows is 9 .