niryuu/sni-each-converted huggingface dataset:
select the rows whose candidates record fuzzily matches to unopposed . select the row whose first elected record of these rows is 1st minimum . the incumbent record of this row is john h kerr .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; candidates ; unopposed } ; first elected ; 1 } ; incumbent } ; john h kerr }
select the rows whose laps record is equal to 58 . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; laps ; 58 } } ; 5 }
select the rows whose ship record fuzzily matches to nagara . take the date record of this row . select the rows whose ship record fuzzily matches to moldanger . take the date record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; ship ; nagara } ; date } ; hop { filter_eq { all_rows ; ship ; moldanger } ; date } }
select the row whose torque record of all rows is 2nd minimum . the model record of this row is 320td ( diesel ) .
Output: eq { hop { nth_argmin { all_rows ; torque ; 2 } ; model } ; 320td ( diesel ) }
select the rows whose school record fuzzily matches to martinsville . take the enrollment record of this row . select the rows whose school record fuzzily matches to mooresville . take the enrollment record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; school ; martinsville } ; enrollment } ; hop { filter_eq { all_rows ; school ; mooresville } ; enrollment } }
select the rows whose matches record is greater than or equal to 3 . for the average records of these rows , most of them are greater than or equal to 8 .
Output: most_greater_eq { filter_greater_eq { all_rows ; matches ; 3 } ; average ; 8 }
select the row whose attendance record of all rows is maximum . the week record of this row is 5 .
Output: eq { hop { argmax { all_rows ; attendance } ; week } ; 5 }
select the rows whose races record is equal to 20 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; races ; 20 } } ; 2 }
select the row whose enrollment record of all rows is maximum . the high school record of this row is mount tahoma .
Output: eq { hop { argmax { all_rows ; enrollment } ; high school } ; mount tahoma }
select the row whose minutes played record of all rows is maximum . the year record of this row is 1999 .
Output: eq { hop { argmax { all_rows ; minutes played } ; year } ; 1999 }
select the row whose votes record of all rows is maximum . the artist record of this row is sahlene .
Output: eq { hop { argmax { all_rows ; votes } ; artist } ; sahlene }
select the rows whose time / retired record fuzzily matches to off course . there is only one such row in the table . the driver record of this unqiue row is gastón mazzacane .
Output: and { only { filter_eq { all_rows ; time / retired ; off course } } ; eq { hop { filter_eq { all_rows ; time / retired ; off course } ; driver } ; gastón mazzacane } }
select the row whose crowd record of all rows is 1st maximum . the venue record of this row is brunswick street oval .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; brunswick street oval }
the average of the episode record of all rows is 32.5 .
Output: round_eq { avg { all_rows ; episode } ; 32.5 }
select the rows whose result record fuzzily matches to nominated . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; result ; nominated } } ; 4 }
select the rows whose unit record is equal to 23 . there is only one such row in the table .
Output: only { filter_eq { all_rows ; unit ; 23 } }
select the rows whose role record fuzzily matches to guest . there is only one such row in the table .
Output: only { filter_eq { all_rows ; role ; guest } }
select the rows whose position record fuzzily matches to cb . there is only one such row in the table . the player record of this unqiue row is dounia whitehouse .
Output: and { only { filter_eq { all_rows ; position ; cb } } ; eq { hop { filter_eq { all_rows ; position ; cb } ; player } ; dounia whitehouse } }
select the rows whose site record fuzzily matches to memorial stadium minneapolis , mn . the maximum attendance record of these rows is 58000 .
Output: eq { max { filter_eq { all_rows ; site ; memorial stadium minneapolis , mn } ; attendance } ; 58000 }
the average of the home team score record of all rows is 13.98 .
Output: round_eq { avg { all_rows ; home team score } ; 13.98 }
select the rows whose country record fuzzily matches to united states . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; country ; united states } } ; 7 }
select the rows whose fatalities record is equal to 0 . there is only one such row in the table . the date record of this unqiue row is 2 february 1969 .
Output: and { only { filter_eq { all_rows ; fatalities ; 0 } } ; eq { hop { filter_eq { all_rows ; fatalities ; 0 } ; date } ; 2 february 1969 } }
select the rows whose 2006 - 10 record is equal to 0.29 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; 2006 - 10 ; 0.29 } } ; 3 }
select the rows whose venue record fuzzily matches to china . there is only one such row in the table .
Output: only { filter_eq { all_rows ; venue ; china } }
select the rows whose attendance record is greater than 25000 . among these rows , select the rows whose date record fuzzily matches to october . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_greater { all_rows ; attendance ; 25000 } ; date ; october } } ; 3 }
select the rows whose podiums record is equal to 4 . there is only one such row in the table . the season record of this unqiue row is 2008 .
Output: and { only { filter_eq { all_rows ; podiums ; 4 } } ; eq { hop { filter_eq { all_rows ; podiums ; 4 } ; season } ; 2008 } }
select the rows whose city of license record fuzzily matches to norton , virginia . take the frequency mhz record of this row . select the rows whose city of license record fuzzily matches to pound , virginia . take the frequency mhz record of this row . the first record is 0.4 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; city of license ; norton , virginia } ; frequency mhz } ; hop { filter_eq { all_rows ; city of license ; pound , virginia } ; frequency mhz } } ; 0.4 }
select the rows whose equipment record fuzzily matches to husaberg - wht . there is only one such row in the table . the driver / passenger record of this unqiue row is daniel millard / joe millard .
Output: and { only { filter_eq { all_rows ; equipment ; husaberg - wht } } ; eq { hop { filter_eq { all_rows ; equipment ; husaberg - wht } ; driver / passenger } ; daniel millard / joe millard } }
select the rows whose result record fuzzily matches to lost re - election democratic gain . there is only one such row in the table . the incumbent record of this unqiue row is fred e busbey .
Output: and { only { filter_eq { all_rows ; result ; lost re - election democratic gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election democratic gain } ; incumbent } ; fred e busbey } }
select the rows whose location attendance record fuzzily matches to pepsi center . select the row whose location attendance record of these rows is maximum . the date record of this row is november 1 .
Output: eq { hop { argmax { filter_eq { all_rows ; location attendance ; pepsi center } ; location attendance } ; date } ; november 1 }
select the row whose uci protour points record of all rows is maximum . the country record of this row is spain .
Output: eq { hop { argmax { all_rows ; uci protour points } ; country } ; spain }
select the rows whose adopted record fuzzily matches to 1999 . among these rows , select the rows whose population ( 2011 - 01 - 01 ) record is less than 1000000 . there is only one such row in the table . the state record of this unqiue row is luxembourg .
Output: and { only { filter_less { filter_eq { all_rows ; adopted ; 1999 } ; population ( 2011 - 01 - 01 ) ; 1000000 } } ; eq { hop { filter_less { filter_eq { all_rows ; adopted ; 1999 } ; population ( 2011 - 01 - 01 ) ; 1000000 } ; state } ; luxembourg } }
select the rows whose high assists record fuzzily matches to rafer alston . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; high assists ; rafer alston } } ; 4 }
select the row whose first elected record of all rows is 2nd minimum . the district record of this row is louisiana 2 .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; district } ; louisiana 2 }
select the rows whose first elected record fuzzily matches to 195 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; first elected ; 195 } } ; 2 }
select the rows whose year record is equal to 2006 . the average of the margin record of these rows is 3.5 .
Output: round_eq { avg { filter_eq { all_rows ; year ; 2006 } ; margin } ; 3.5 }
select the rows whose year record fuzzily matches to 2005 . take the money ( ￥ ) record of this row . select the rows whose year record fuzzily matches to 2006 . take the money ( ￥ ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 2005 } ; money ( ￥ ) } ; hop { filter_eq { all_rows ; year ; 2006 } ; money ( ￥ ) } }
select the rows whose candidates record fuzzily matches to unopposed . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; candidates ; unopposed } } ; 5 }
select the rows whose final position / round record fuzzily matches to runner - up . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; final position / round ; runner - up } } ; 4 }
select the rows whose fixtures record is equal to 8 . there is only one such row in the table . the round record of this unqiue row is fifth round .
Output: and { only { filter_eq { all_rows ; fixtures ; 8 } } ; eq { hop { filter_eq { all_rows ; fixtures ; 8 } ; round } ; fifth round } }
the average of the area ( km square ) record of all rows is 47 .
Output: round_eq { avg { all_rows ; area ( km square ) } ; 47 }
select the rows whose gymnast record fuzzily matches to ( chn ) . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; gymnast ; ( chn ) } } ; 2 }
select the row whose rank record of all rows is 7th minimum . the nation record of this row is bulgaria .
Output: eq { hop { nth_argmin { all_rows ; rank ; 7 } ; nation } ; bulgaria }
select the row whose frequency mhz record of all rows is 2nd maximum . the call sign record of this row is w291aq .
Output: eq { hop { nth_argmax { all_rows ; frequency mhz ; 2 } ; call sign } ; w291aq }
select the rows whose time record fuzzily matches to accident . there is only one such row in the table . the rider record of this unqiue row is kousuke akiyoshi .
Output: and { only { filter_eq { all_rows ; time ; accident } } ; eq { hop { filter_eq { all_rows ; time ; accident } ; rider } ; kousuke akiyoshi } }
the average of the wins record of all rows is .12 .
Output: round_eq { avg { all_rows ; wins } ; .12 }
select the rows whose incumbent record fuzzily matches to newt v mills . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to jared y sanders , jr . take the first elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incumbent ; newt v mills } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; jared y sanders , jr } ; first elected } }
select the rows whose country record fuzzily matches to nigeria . among these rows , select the rows whose country record fuzzily matches to nigeria . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; country ; nigeria } ; country ; nigeria } } ; 2 }
for the storyteller records of all rows , most of them fuzzily match to tucker .
Output: most_eq { all_rows ; storyteller ; tucker }
the average of the score record of all rows is 139 .
Output: round_eq { avg { all_rows ; score } ; 139 }
the 4th minimum date record of all rows is december 20 . the team record of the row with 4th minimum date record is kansas .
Output: and { eq { nth_min { all_rows ; date ; 4 } ; december 20 } ; eq { hop { nth_argmin { all_rows ; date ; 4 } ; team } ; kansas } }
select the rows whose acquired record is greater than 2001 . select the row whose capacity , mln tpa record of these rows is minimum . the name record of this row is trn .
Output: eq { hop { argmin { filter_greater { all_rows ; acquired ; 2001 } ; capacity , mln tpa } ; name } ; trn }
for the position records of all rows , most of them fuzzily match to tackle .
Output: most_eq { all_rows ; position ; tackle }
select the rows whose platform ( s ) record fuzzily matches to playstation 3 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; platform ( s ) ; playstation 3 } } ; 3 }
select the row whose gold record of all rows is maximum . the nation record of this row is great britain .
Output: eq { hop { argmax { all_rows ; gold } ; nation } ; great britain }
for the first elected records of all rows , most of them are greater than 1920 .
Output: most_greater { all_rows ; first elected ; 1920 }
select the rows whose date record fuzzily matches to september . the sum of the attendance record of these rows is 226814 .
Output: round_eq { sum { filter_eq { all_rows ; date ; september } ; attendance } ; 226814 }
select the rows whose points record is equal to 10 . there is only one such row in the table . the entrant record of this unqiue row is braun tyrrell honda .
Output: and { only { filter_eq { all_rows ; points ; 10 } } ; eq { hop { filter_eq { all_rows ; points ; 10 } ; entrant } ; braun tyrrell honda } }
for the overall records of all rows , most of them are less than 10 .
Output: most_less { all_rows ; overall ; 10 }
select the row whose position record of all rows is minimum . the year record of this row is 2009 .
Output: eq { hop { argmin { all_rows ; position } ; year } ; 2009 }
select the rows whose census ranking record is greater than 1000 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; census ranking ; 1000 } } ; 3 }
select the rows whose surface record fuzzily matches to clay . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; surface ; clay } } ; 4 }
select the rows whose outgoing manager record fuzzily matches to kevin bond . take the date of vacancy record of this row . select the rows whose outgoing manager record fuzzily matches to alan buckley . take the date of vacancy record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; outgoing manager ; kevin bond } ; date of vacancy } ; hop { filter_eq { all_rows ; outgoing manager ; alan buckley } ; date of vacancy } }
for the story by records of all rows , all of them fuzzily match to david simon & ed burns .
Output: all_eq { all_rows ; story by ; david simon & ed burns }
select the row whose place record of all rows is minimum . the player record of this row is chris dimarco .
Output: eq { hop { argmin { all_rows ; place } ; player } ; chris dimarco }
select the rows whose period active record fuzzily matches to 199 . select the row whose claimed sales record of these rows is maximum . the artist record of this row is the black eyed peas .
Output: eq { hop { argmax { filter_eq { all_rows ; period active ; 199 } ; claimed sales } ; artist } ; the black eyed peas }
the average of the position record of all rows is 100 .
Output: round_eq { avg { all_rows ; position } ; 100 }
select the rows whose time record is less than 2:00 . the number of such rows is 5 .
Output: eq { count { filter_less { all_rows ; time ; 2:00 } } ; 5 }
for the start source records of all rows , most of them fuzzily match to bbc sport .
Output: most_eq { all_rows ; start source ; bbc sport }
select the row whose born - died record of all rows is 2nd minimum . the name record of this row is sali berisha .
Output: eq { hop { nth_argmin { all_rows ; born - died ; 2 } ; name } ; sali berisha }
for the result records of all rows , all of them fuzzily match to re - elected .
Output: all_eq { all_rows ; result ; re - elected }
select the row whose runs record of all rows is 2nd maximum . the partnerships record of this row is herschelle gibbs / justin kemp .
Output: eq { hop { nth_argmax { all_rows ; runs ; 2 } ; partnerships } ; herschelle gibbs / justin kemp }
select the row whose home team score record of all rows is maximum . the home team record of this row is hawthorn .
Output: eq { hop { argmax { all_rows ; home team score } ; home team } ; hawthorn }
select the row whose time record of all rows is 5th minimum . the athlete record of this row is craig virgin .
Output: eq { hop { nth_argmin { all_rows ; time ; 5 } ; athlete } ; craig virgin }
for the decision records of all rows , most of them fuzzily match to biron .
Output: most_eq { all_rows ; decision ; biron }
select the row whose total record of all rows is 2nd maximum . the athlete record of this row is monica pinette .
Output: eq { hop { nth_argmax { all_rows ; total ; 2 } ; athlete } ; monica pinette }
select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to l .
Output: most_eq { filter_eq { all_rows ; date ; october } ; result ; l }
the 2nd minimum match number record of all rows is 2 . the date record of the row with 2nd minimum match number record is april 10 . the team 1 record of the row with 2nd minimum match number record is icl pakistan . the team 2 record of the row with 2nd minimum match number record is icl world .
Output: and { eq { nth_min { all_rows ; match number ; 2 } ; 2 } ; and { eq { hop { nth_argmin { all_rows ; match number ; 2 } ; date } ; april 10 } ; and { eq { hop { nth_argmin { all_rows ; match number ; 2 } ; team 1 } ; icl pakistan } ; eq { hop { nth_argmin { all_rows ; match number ; 2 } ; team 2 } ; icl world } } } }
select the rows whose incumbent record fuzzily matches to wilbur mills . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to james william trimble . take the first elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incumbent ; wilbur mills } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; james william trimble } ; first elected } }
for the title records of all rows , most of them fuzzily match to duke .
Output: most_eq { all_rows ; title ; duke }
select the rows whose reason for change record fuzzily matches to died . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; reason for change ; died } } ; 3 }
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 .
Output: eq { count { filter_eq { filter_less { all_rows ; year ; 1998 } ; country ; sweden } } ; 1 }
select the row whose population ( in 2008 ) record of all rows is 2nd minimum . the suburb record of this row is phillip .
Output: eq { hop { nth_argmin { all_rows ; population ( in 2008 ) ; 2 } ; suburb } ; phillip }
select the rows whose team record fuzzily matches to walker racing . there is only one such row in the table . the driver record of this unqiue row is mario haberfeld .
Output: and { only { filter_eq { all_rows ; team ; walker racing } } ; eq { hop { filter_eq { all_rows ; team ; walker racing } ; driver } ; mario haberfeld } }
select the rows whose artist record fuzzily matches to dire straits . take the claimed sales record of this row . select the rows whose artist record fuzzily matches to barbra streisand . take the claimed sales record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; artist ; dire straits } ; claimed sales } ; hop { filter_eq { all_rows ; artist ; barbra streisand } ; claimed sales } }
select the row whose date record of all rows is minimum . the circuit record of this row is calder park raceway .
Output: eq { hop { argmin { all_rows ; date } ; circuit } ; calder park raceway }
the sum of the points record of all rows is 636 .
Output: round_eq { sum { all_rows ; points } ; 636 }
select the rows whose attendance record is greater than 50000 . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater { all_rows ; attendance ; 50000 } ; result ; w } } ; 2 }
select the rows whose reason for change record fuzzily matches to to enter the us army . there is only one such row in the table . the vacator record of this unqiue row is charles i faddis ( d ) .
Output: and { only { filter_eq { all_rows ; reason for change ; to enter the us army } } ; eq { hop { filter_eq { all_rows ; reason for change ; to enter the us army } ; vacator } ; charles i faddis ( d ) } }
select the row whose enrollment record of all rows is maximum . the institution record of this row is university of new hampshire club sports .
Output: eq { hop { argmax { all_rows ; enrollment } ; institution } ; university of new hampshire club sports }
select the rows whose date record is equal to 1939 . there is only one such row in the table . the name record of this unqiue row is garrett .
Output: and { only { filter_eq { all_rows ; date ; 1939 } } ; eq { hop { filter_eq { all_rows ; date ; 1939 } ; name } ; garrett } }
select the rows whose date record fuzzily matches to december . among these rows , select the rows whose length of game record is less than 80:00 . the number of such rows is 2 .
Output: eq { count { filter_less { filter_eq { all_rows ; date ; december } ; length of game ; 80:00 } } ; 2 }
the sum of the attendance record of all rows is 881037 .
Output: round_eq { sum { all_rows ; attendance } ; 881037 }
the 1st minimum pos record of all rows is 1 . the diff record of the row with 1st minimum pos record is + 176 .
Output: and { eq { nth_min { all_rows ; pos ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; pos ; 1 } ; diff } ; + 176 } }
select the rows whose successor record fuzzily matches to thomas tillinghast ( f ) . take the date successor seated record of this row . select the rows whose successor record fuzzily matches to robert waln ( f ) . take the date successor seated record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; successor ; thomas tillinghast ( f ) } ; date successor seated } ; hop { filter_eq { all_rows ; successor ; robert waln ( f ) } ; date successor seated } }
for the ends records of all rows , most of them are equal to 2010 .
Output: most_eq { all_rows ; ends ; 2010 }
the sum of the apps record of all rows is 137 .
Output: round_eq { sum { all_rows ; apps } ; 137 }
the average of the pick record of all rows is 15 .
Output: round_eq { avg { all_rows ; pick } ; 15 }
select the rows whose crowd record is greater than 10000 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; crowd ; 10000 } } ; 2 }
select the rows whose elevation ( m ) record is less than 2400 . there is only one such row in the table . the peak record of this unqiue row is pietrosul rodnei .
Output: and { only { filter_less { all_rows ; elevation ( m ) ; 2400 } } ; eq { hop { filter_less { all_rows ; elevation ( m ) ; 2400 } ; peak } ; pietrosul rodnei } }
select the rows whose year record fuzzily matches to junior . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; year ; junior } } ; 3 }
select the row whose population ( 2011 ) record of all rows is maximum . the settlement record of this row is srpska crnja .
Output: eq { hop { argmax { all_rows ; population ( 2011 ) } ; settlement } ; srpska crnja }
select the rows whose region record fuzzily matches to kansai . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; region ; kansai } } ; 2 }
the sum of the pts record of all rows is 23 .
Output: round_eq { sum { all_rows ; pts } ; 23 }
select the rows whose episode record fuzzily matches to 4 . take the ratings ( kanto ) record of this row . select the rows whose episode record fuzzily matches to 3 . take the ratings ( kanto ) record of this row . the first record is less than the second record . the ratings ( kanto ) record of the first row is 4.3 . the ratings ( kanto ) record of the second row is 4.6 .
Output: and { less { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } } ; and { eq { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; 4.3 } ; eq { hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } ; 4.6 } } }
select the rows whose venue record fuzzily matches to bursa atatürk stadium . take the capacity record of this row . select the rows whose venue record fuzzily matches to şükrü saracoğlu stadium . take the capacity record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; venue ; bursa atatürk stadium } ; capacity } ; hop { filter_eq { all_rows ; venue ; şükrü saracoğlu stadium } ; capacity } }
select the rows whose height record fuzzily matches to 6-4 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; height ; 6-4 } } ; 4 }
select the row whose points for record of all rows is maximum . the team record of this row is swansea .
Output: eq { hop { argmax { all_rows ; points for } ; team } ; swansea }
select the row whose attendance record of all rows is 2nd maximum . the date record of this row is november 22 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; november 22 }
select the row whose first air date record of all rows is minimum . the city record of this row is townsville .
Output: eq { hop { argmin { all_rows ; first air date } ; city } ; townsville }
select the rows whose winning party / coalition record fuzzily matches to indian national congress . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; winning party / coalition ; indian national congress } } ; 3 }
select the row whose total record of all rows is 2nd maximum . the nation record of this row is japan ( jpn ) .
Output: eq { hop { nth_argmax { all_rows ; total ; 2 } ; nation } ; japan ( jpn ) }
select the rows whose partner record fuzzily matches to alexander krasnorutskiy . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; partner ; alexander krasnorutskiy } } ; 5 }
the maximum took office record of all rows is 27 july 2011 .
Output: eq { max { all_rows ; took office } ; 27 july 2011 }
select the rows whose school record fuzzily matches to avon community . take the enrollment record of this row . select the rows whose school record fuzzily matches to brownsburg . take the enrollment record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; school ; avon community } ; enrollment } ; hop { filter_eq { all_rows ; school ; brownsburg } ; enrollment } }
select the row whose capacity record of all rows is 3rd maximum . the stadium record of this row is stadion gradski vrt .
Output: eq { hop { nth_argmax { all_rows ; capacity ; 3 } ; stadium } ; stadion gradski vrt }
select the row whose points record of all rows is 1st maximum . the player record of this row is bill daley .
Output: eq { hop { nth_argmax { all_rows ; points ; 1 } ; player } ; bill daley }
select the rows whose games record is greater than 700 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; games ; 700 } } ; 2 }
select the row whose year built record of all rows is minimum . the church name record of this row is urnes stavkyrkje .
Output: eq { hop { argmin { all_rows ; year built } ; church name } ; urnes stavkyrkje }
the average of the apparent magnitude record of all rows is 12.9 .
Output: round_eq { avg { all_rows ; apparent magnitude } ; 12.9 }
for the first elected records of all rows , most of them are equal to 1940 .
Output: most_eq { all_rows ; first elected ; 1940 }
select the row whose attendance record of all rows is 2nd maximum . the date record of this row is november 12 , 2000 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; november 12 , 2000 }
select the rows whose award record fuzzily matches to laurence olivier award . take the category record of this row . select the rows whose award record fuzzily matches to theatregoers ' choice award . take the category record of this row . the first record fuzzily matches to the second record . the category record of the first row is best actress in a musical . the category record of the second row is best actress in a musical .
Output: and { eq { hop { filter_eq { all_rows ; award ; laurence olivier award } ; category } ; hop { filter_eq { all_rows ; award ; theatregoers ' choice award } ; category } } ; and { eq { hop { filter_eq { all_rows ; award ; laurence olivier award } ; category } ; best actress in a musical } ; eq { hop { filter_eq { all_rows ; award ; theatregoers ' choice award } ; category } ; best actress in a musical } } }
select the row whose number of viewers record of all rows is maximum . the show record of this row is 1966 world cup final .
Output: eq { hop { argmax { all_rows ; number of viewers } ; show } ; 1966 world cup final }
select the row whose points record of all rows is maximum . the team record of this row is alianza fc .
Output: eq { hop { argmax { all_rows ; points } ; team } ; alianza fc }
select the rows whose engine record fuzzily matches to judd v8 . there is only one such row in the table . the year record of this unqiue row is 1989 .
Output: and { only { filter_eq { all_rows ; engine ; judd v8 } } ; eq { hop { filter_eq { all_rows ; engine ; judd v8 } ; year } ; 1989 } }
for the partner records of all rows , most of them fuzzily match to sherwood stewart .
Output: most_eq { all_rows ; partner ; sherwood stewart }
select the rows whose opponent record fuzzily matches to new york giants . there is only one such row in the table . the week record of this unqiue row is 1 .
Output: and { only { filter_eq { all_rows ; opponent ; new york giants } } ; eq { hop { filter_eq { all_rows ; opponent ; new york giants } ; week } ; 1 } }
the average of the time record of all rows is 8:17.94 .
Output: round_eq { avg { all_rows ; time } ; 8:17.94 }
for the 3:26.00 records of all rows , most of them are less than 3:30 .
Output: most_less { all_rows ; 3:26.00 ; 3:30 }
for the age records of all rows , most of them are greater than 18 .
Output: most_greater { all_rows ; age ; 18 }
for the construction completed records of all rows , most of them fuzzily match to - .
Output: most_eq { all_rows ; construction completed ; - }
the 1st minimum date of vacancy record of all rows is 7 sep 2010 . the outgoing head coach record of the row with 1st minimum date of vacancy record is rasoul korbekandi .
Output: and { eq { nth_min { all_rows ; date of vacancy ; 1 } ; 7 sep 2010 } ; eq { hop { nth_argmin { all_rows ; date of vacancy ; 1 } ; outgoing head coach } ; rasoul korbekandi } }
select the rows whose date record fuzzily matches to june . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; date ; june } } ; 2 }
the average of the crowd record of all rows is 13841 .
Output: round_eq { avg { all_rows ; crowd } ; 13841 }
the average of the attendance record of all rows is 58252 .
Output: round_eq { avg { all_rows ; attendance } ; 58252 }
select the rows whose opponent record fuzzily matches to philadelphia soul . take the date record of this row . select the rows whose opponent record fuzzily matches to georgia force . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; philadelphia soul } ; date } ; hop { filter_eq { all_rows ; opponent ; georgia force } ; date } }
select the rows whose director record fuzzily matches to hassard short . there is only one such row in the table . the date record of this unqiue row is june 3 , 1931 .
Output: and { only { filter_eq { all_rows ; director ; hassard short } } ; eq { hop { filter_eq { all_rows ; director ; hassard short } ; date } ; june 3 , 1931 } }
the average of the design flow ( lpm ) record of all rows is 823.57 .
Output: round_eq { avg { all_rows ; design flow ( lpm ) } ; 823.57 }
select the rows whose 1961 democratic primary record fuzzily matches to robert f wagner , jr . take the total record of this row . select the rows whose 1961 democratic primary record fuzzily matches to arthur levitt . take the total record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; 1961 democratic primary ; robert f wagner , jr } ; total } ; hop { filter_eq { all_rows ; 1961 democratic primary ; arthur levitt } ; total } }
select the rows whose founded record is greater than 1850 . the average of the enrollment record of these rows is 2939 .
Output: round_eq { avg { filter_greater { all_rows ; founded ; 1850 } ; enrollment } ; 2939 }
the average of the score record of all rows is 74.8 .
Output: round_eq { avg { all_rows ; score } ; 74.8 }
select the rows whose nation record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; nation } } ; 6 }
select the row whose year record of all rows is 2nd maximum . the new jersey record of this row is paramus ll paramus .
Output: eq { hop { nth_argmax { all_rows ; year ; 2 } ; new jersey } ; paramus ll paramus }
select the row whose away team score record of all rows is maximum . the home team score record of this row is 15.21 ( 111 ) .
Output: eq { hop { argmax { all_rows ; away team score } ; home team score } ; 15.21 ( 111 ) }
for the distance / duration records of all rows , all of them are greater than 40 laps .
Output: all_greater { all_rows ; distance / duration ; 40 laps }
for the ihsaa class records of all rows , all of them fuzzily match to aaaa .
Output: all_eq { all_rows ; ihsaa class ; aaaa }
select the row whose away team score record of all rows is 2nd maximum . the home team record of this row is collingwood .
Output: eq { hop { nth_argmax { all_rows ; away team score ; 2 } ; home team } ; collingwood }
select the row whose administrative panel record of all rows is maximum . the party record of this row is fine gael .
Output: eq { hop { argmax { all_rows ; administrative panel } ; party } ; fine gael }
select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record fuzzily matches to 138 . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; score ; 138 } } ; 4 }
select the rows whose location record fuzzily matches to nc . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location ; nc } } ; 3 }
select the rows whose nation record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; nation } } ; 5 }
select the row whose gold record of all rows is 2nd maximum . the nation record of this row is argentina ( arg ) .
Output: eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; argentina ( arg ) }
select the row whose research funding ( 000 ) record of all rows is maximum . the institution record of this row is loughborough university .
Output: eq { hop { argmax { all_rows ; research funding ( 000 ) } ; institution } ; loughborough university }
select the rows whose no in series record is arbitrary . the number of such rows is 11 .
Output: eq { count { filter_all { all_rows ; no in series } } ; 11 }
select the rows whose result record fuzzily matches to draw . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; result ; draw } } ; 2 }
select the rows whose game record fuzzily matches to 1 . take the date record of this row . select the rows whose game record fuzzily matches to 2 . take the date record of this row . the second record is 2 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; game ; 1 } ; date } ; hop { filter_eq { all_rows ; game ; 2 } ; date } } ; -2 }
the average of the viewers ( in millions ) record of all rows is 9.1 .
Output: round_eq { avg { all_rows ; viewers ( in millions ) } ; 9.1 }
the 3rd maximum winnings record of all rows is 160261 .
Output: eq { nth_max { all_rows ; winnings ; 3 } ; 160261 }
select the rows whose seasons record is equal to 4 . there is only one such row in the table . the country record of this unqiue row is south korea .
Output: and { only { filter_eq { all_rows ; seasons ; 4 } } ; eq { hop { filter_eq { all_rows ; seasons ; 4 } ; country } ; south korea } }
select the row whose per capita withdrawal record of all rows is maximum . the record of this row is turkmenistan .
Output: eq { hop { argmax { all_rows ; per capita withdrawal } ; } ; turkmenistan }
select the rows whose nba draft record fuzzily matches to clippers . there is only one such row in the table .
Output: only { filter_eq { all_rows ; nba draft ; clippers } }
the average of the goals against record of all rows is 53 .
Output: round_eq { avg { all_rows ; goals against } ; 53 }
the average of the away team score record of all rows is 13.81 .
Output: round_eq { avg { all_rows ; away team score } ; 13.81 }
select the row whose transfer fee record of all rows is 2nd maximum . the name record of this row is snodgrass .
Output: eq { hop { nth_argmax { all_rows ; transfer fee ; 2 } ; name } ; snodgrass }
select the rows whose visitor record fuzzily matches to colorado . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; visitor ; colorado } } ; 3 }
select the rows whose location record fuzzily matches to usa . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; location ; usa } } ; 6 }
select the row whose left record of all rows is minimum . the institution record of this row is east carolina university .
Output: eq { hop { argmin { all_rows ; left } ; institution } ; east carolina university }
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 .
Output: less { hop { filter_eq { all_rows ; player ; hakim akbar } ; round } ; hop { filter_eq { all_rows ; player ; leonard myers } ; round } }
the average of the top speed ( in operation ) （ km / h ） record of all rows is 106.33 .
Output: round_eq { avg { all_rows ; top speed ( in operation ) （ km / h ） } ; 106.33 }
select the row whose opened record of all rows is maximum . the name record of this row is ben 10 - ultimate mission .
Output: eq { hop { argmax { all_rows ; opened } ; name } ; ben 10 - ultimate mission }
select the rows whose college record fuzzily matches to florida state . there is only one such row in the table . the name record of this unqiue row is e g green .
Output: and { only { filter_eq { all_rows ; college ; florida state } } ; eq { hop { filter_eq { all_rows ; college ; florida state } ; name } ; e g green } }
select the row whose catches record of all rows is maximum . the player record of this row is m kaif .
Output: eq { hop { argmax { all_rows ; catches } ; player } ; m kaif }
the average of the points record of all rows is 111 .
Output: round_eq { avg { all_rows ; points } ; 111 }
select the row whose avg attend record of all rows is 2nd maximum . the indoor year record of this row is 1978 .
Output: eq { hop { nth_argmax { all_rows ; avg attend ; 2 } ; indoor year } ; 1978 }
the average of the age record of all rows is 25.6 .
Output: round_eq { avg { all_rows ; age } ; 25.6 }
select the rows whose partner record fuzzily matches to philipp oswald . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; partner ; philipp oswald } } ; 2 }
select the rows whose date record fuzzily matches to 14 jun 2005 . take the margin of victory record of this row . select the rows whose date record fuzzily matches to 12 jul 2006 . take the margin of victory record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; 14 jun 2005 } ; margin of victory } ; hop { filter_eq { all_rows ; date ; 12 jul 2006 } ; margin of victory } }
select the rows whose model record fuzzily matches to amd 800 chipset series . take the tdp ( w ) record of this row . select the rows whose model record fuzzily matches to amd 700 chipset series . take the tdp ( w ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; model ; amd 800 chipset series } ; tdp ( w ) } ; hop { filter_eq { all_rows ; model ; amd 700 chipset series } ; tdp ( w ) } }
select the rows whose co - drivers record fuzzily matches to olivier pla guy smith . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; co - drivers ; olivier pla guy smith } } ; 2 }
for the most wkts records of all rows , most of them fuzzily match to george davidson .
Output: most_eq { all_rows ; most wkts ; george davidson }
select the rows whose results record fuzzily matches to retired to run for us senate democratic hold . there is only one such row in the table . the incumbent record of this unqiue row is harold ford jr .
Output: and { only { filter_eq { all_rows ; results ; retired to run for us senate democratic hold } } ; eq { hop { filter_eq { all_rows ; results ; retired to run for us senate democratic hold } ; incumbent } ; harold ford jr } }
select the rows whose namesake record fuzzily matches to sumerian town . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; namesake ; sumerian town } } ; 2 }
the sum of the televotes record of all rows is 147968 .
Output: round_eq { sum { all_rows ; televotes } ; 147968 }
for the region records of all rows , all of them fuzzily match to japan .
Output: all_eq { all_rows ; region ; japan }
select the rows whose position record fuzzily matches to guard . for the number records of these rows , most of them are less than 20 .
Output: most_less { filter_eq { all_rows ; position ; guard } ; number ; 20 }
the average of the crowd record of all rows is 22,800 .
Output: round_eq { avg { all_rows ; crowd } ; 22,800 }
for the completed records of all rows , all of them fuzzily match to no .
Output: all_eq { all_rows ; completed ; no }
select the rows whose rank record is less than or equal to 10 . for the worldwide gross records of these rows , all of them are greater than 300,000,000 .
Output: all_greater { filter_less_eq { all_rows ; rank ; 10 } ; worldwide gross ; 300,000,000 }
select the rows whose original air date record fuzzily matches to april . for the us viewers ( million ) records of these rows , most of them are greater than or equal to 9 .
Output: most_greater_eq { filter_eq { all_rows ; original air date ; april } ; us viewers ( million ) ; 9 }
select the rows whose tdp record fuzzily matches to 17 w . there is only one such row in the table . the brand name ( list ) record of this unqiue row is core i7 - 3xx7u , i7 - 3xx7ue .
Output: and { only { filter_eq { all_rows ; tdp ; 17 w } } ; eq { hop { filter_eq { all_rows ; tdp ; 17 w } ; brand name ( list ) } ; core i7 - 3xx7u , i7 - 3xx7ue } }
select the rows whose country record does not match to china / hong kong . among these rows , select the rows whose year record is less than 2004 . the number of such rows is 4 .
Output: eq { count { filter_less { filter_not_eq { all_rows ; country ; china / hong kong } ; year ; 2004 } } ; 4 }
select the rows whose best record is greater than 1:25.000 . the number of such rows is 6 .
Output: eq { count { filter_greater { all_rows ; best ; 1:25.000 } } ; 6 }
select the rows whose score record fuzzily matches to w . there is only one such row in the table . the date record of this unqiue row is april 20 .
Output: and { only { filter_eq { all_rows ; score ; w } } ; eq { hop { filter_eq { all_rows ; score ; w } ; date } ; april 20 } }
select the row whose round record of all rows is 3rd minimum . the player record of this row is glen irwin .
Output: eq { hop { nth_argmin { all_rows ; round ; 3 } ; player } ; glen irwin }
select the rows whose days record is greater than 110 . there is only one such row in the table . the series record of this unqiue row is season 3 .
Output: and { only { filter_greater { all_rows ; days ; 110 } } ; eq { hop { filter_greater { all_rows ; days ; 110 } ; series } ; season 3 } }
select the rows whose first elected record fuzzily matches to 19 . for the party records of these rows , all of them fuzzily match to democratic .
Output: all_eq { filter_eq { all_rows ; first elected ; 19 } ; party ; democratic }
select the rows whose venue record fuzzily matches to south korea . there is only one such row in the table . the tournament record of this unqiue row is summer olympics .
Output: and { only { filter_eq { all_rows ; venue ; south korea } } ; eq { hop { filter_eq { all_rows ; venue ; south korea } ; tournament } ; summer olympics } }
select the row whose year record of all rows is 2nd minimum . the formula record of this row is grand prix .
Output: eq { hop { nth_argmin { all_rows ; year ; 2 } ; formula } ; grand prix }
select the rows whose away team score record is greater than 13.0 . among these rows , select the rows whose crowd record is greater than 19000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_greater { all_rows ; away team score ; 13.0 } ; crowd ; 19000 } } ; 2 }
select the rows whose sfc in lb / ( lbf h ) record is greater than 10 . there is only one such row in the table . the engine type record of this unqiue row is nk - 33 rocket engine .
Output: and { only { filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 } } ; eq { hop { filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 } ; engine type } ; nk - 33 rocket engine } }
select the rows whose points record is greater than 270 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; points ; 270 } } ; 2 }
select the row whose cars per set record of all rows is 1st maximum . the class record of this row is class 254 .
Output: eq { hop { nth_argmax { all_rows ; cars per set ; 1 } ; class } ; class 254 }
select the rows whose school record fuzzily matches to perry central . take the size record of this row . select the rows whose school record fuzzily matches to springs valley . take the size record of this row . the first record is 15 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; school ; perry central } ; size } ; hop { filter_eq { all_rows ; school ; springs valley } ; size } } ; 15 }
select the row whose rnag ( mhz ) record of all rows is maximum . the transmitter record of this row is clermont carn .
Output: eq { hop { argmax { all_rows ; rnag ( mhz ) } ; transmitter } ; clermont carn }
select the rows whose weapon record fuzzily matches to razor boomerang . there is only one such row in the table . the eagle riders record of this unqiue row is hunter harris .
Output: and { only { filter_eq { all_rows ; weapon ; razor boomerang } } ; eq { hop { filter_eq { all_rows ; weapon ; razor boomerang } ; eagle riders } ; hunter harris } }
select the rows whose nation record fuzzily matches to australia . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; nation ; australia } } ; 4 }
select the row whose individual record of all rows is 2nd minimum . the event record of this row is 2004 oberhof .
Output: eq { hop { nth_argmin { all_rows ; individual ; 2 } ; event } ; 2004 oberhof }
select the rows whose team record fuzzily matches to team jlr . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; team ; team jlr } } ; 4 }
select the rows whose school / club team record fuzzily matches to creighton . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; school / club team ; creighton } } ; 2 }
select the rows whose season record fuzzily matches to 2010 . take the attendance / g record of this row . select the rows whose season record fuzzily matches to 2009 . take the attendance / g record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; season ; 2010 } ; attendance / g } ; hop { filter_eq { all_rows ; season ; 2009 } ; attendance / g } }