niryuu/sni-each-converted huggingface dataset:
select the rows whose score record fuzzily matches to 3 - 0 . take the competition record of this row . select the rows whose score record fuzzily matches to 3 - 2 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is friendly . the competition record of the second row is friendly .
Output: and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; friendly } ; eq { hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } ; friendly } } }
select the rows whose erp w record is equal to 33 . there is only one such row in the table . the call sign record of this unqiue row is w267 am .
Output: and { only { filter_eq { all_rows ; erp w ; 33 } } ; eq { hop { filter_eq { all_rows ; erp w ; 33 } ; call sign } ; w267 am } }
select the rows whose high points record fuzzily matches to vince carter . the number of such rows is 12 .
Output: eq { count { filter_eq { all_rows ; high points ; vince carter } } ; 12 }
select the rows whose game site record fuzzily matches to mile high stadium . the maximum attendance record of these rows is 74970 . the date record of the row with superlative attendance record is sep 21 .
Output: and { eq { max { filter_eq { all_rows ; game site ; mile high stadium } ; attendance } ; 74970 } ; eq { hop { argmax { filter_eq { all_rows ; game site ; mile high stadium } ; attendance } ; date } ; sep 21 } }
select the row whose races record of all rows is maximum . the year record of this row is 2010 .
Output: eq { hop { argmax { all_rows ; races } ; year } ; 2010 }
select the rows whose country record fuzzily matches to scotland . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; country ; scotland } } ; 2 }
select the rows whose transfer fee record is greater than 0 . there is only one such row in the table . the player record of this unqiue row is matthew mills .
Output: and { only { filter_greater { all_rows ; transfer fee ; 0 } } ; eq { hop { filter_greater { all_rows ; transfer fee ; 0 } ; player } ; matthew mills } }
for the event records of all rows , most of them fuzzily match to monsters of rock .
Output: most_eq { all_rows ; event ; monsters of rock }
select the rows whose name record fuzzily matches to still standing . take the launched record of this row . select the rows whose name record fuzzily matches to falling angel . take the launched record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; still standing } ; launched } ; hop { filter_eq { all_rows ; name ; falling angel } ; launched } }
select the rows whose opponent record fuzzily matches to buffalo braves . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; opponent ; buffalo braves } } ; 3 }
select the rows whose introduced record is equal to 1958 . for the withdrawn records of these rows , most of them are less than 1970 .
Output: most_less { filter_eq { all_rows ; introduced ; 1958 } ; withdrawn ; 1970 }
select the rows whose visitor record fuzzily matches to pittsburgh . take the date record of this row . select the rows whose visitor record fuzzily matches to boston . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; visitor ; pittsburgh } ; date } ; hop { filter_eq { all_rows ; visitor ; boston } ; date } }
select the row whose density record of all rows is maximum . the english name record of this row is xinluo district .
Output: eq { hop { argmax { all_rows ; density } ; english name } ; xinluo district }
select the rows whose parishes record is greater than 60 . among these rows , select the rows whose high schools record is less than 5 . the number of such rows is 2 .
Output: eq { count { filter_less { filter_greater { all_rows ; parishes ; 60 } ; high schools ; 5 } } ; 2 }
select the row whose population record of all rows is maximum . the official name record of this row is moncton .
Output: eq { hop { argmax { all_rows ; population } ; official name } ; moncton }
select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . select the rows whose home team record fuzzily matches to essendon . take the home team score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } ; hop { filter_eq { all_rows ; home team ; essendon } ; home team score } }
select the row whose crowd record of all rows is 1st maximum . the venue record of this row is lake oval .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; lake oval }
select the row whose release - year of first charted record record of all rows is minimum . the artist record of this row is frank sinatra .
Output: eq { hop { argmin { all_rows ; release - year of first charted record } ; artist } ; frank sinatra }
select the row whose tdp record of all rows is 2nd maximum . the brand name ( list ) record of this row is core i3 - 32xx .
Output: eq { hop { nth_argmax { all_rows ; tdp ; 2 } ; brand name ( list ) } ; core i3 - 32xx }
for the current status records of all rows , most of them fuzzily match to active .
Output: most_eq { all_rows ; current status ; active }
select the row whose rank record of all rows is maximum . the name record of this row is environmental sustainability index .
Output: eq { hop { argmax { all_rows ; rank } ; name } ; environmental sustainability index }
select the row whose against record of all rows is maximum . the opposing team record of this row is giqualand west .
Output: eq { hop { argmax { all_rows ; against } ; opposing team } ; giqualand west }
select the rows whose points record is equal to 16 . there is only one such row in the table . the team record of this unqiue row is ec são caetano .
Output: and { only { filter_eq { all_rows ; points ; 16 } } ; eq { hop { filter_eq { all_rows ; points ; 16 } ; team } ; ec são caetano } }
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 .
Output: greater { hop { filter_eq { all_rows ; date ; september 6 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 22 } ; attendance } }
select the rows whose year record is less than 1967 . among these rows , select the rows whose result record fuzzily matches to 2nd . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_less { all_rows ; year ; 1967 } ; result ; 2nd } } ; 2 }
for the status records of all rows , all of them fuzzily match to running .
Output: all_eq { all_rows ; status ; running }
select the rows whose location attendance record fuzzily matches to us airways center 18422 . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; location attendance ; us airways center 18422 } } ; 7 }
select the row whose price record of all rows is 2nd minimum . the internet plan record of this row is internet 30 .
Output: eq { hop { nth_argmin { all_rows ; price ; 2 } ; internet plan } ; internet 30 }
the average of the 1 - year peak record of all rows is 2869 .
Output: round_eq { avg { all_rows ; 1 - year peak } ; 2869 }
select the rows whose viewership record is greater than 6.00 million . the number of such rows is 4 .
Output: eq { count { filter_greater { all_rows ; viewership ; 6.00 million } } ; 4 }
for the open cup records of all rows , all of them fuzzily match to did not enter .
Output: all_eq { all_rows ; open cup ; did not enter }
select the row whose vuelta record of all rows is maximum . the name record of this row is tony rominger ( sui ) .
Output: eq { hop { argmax { all_rows ; vuelta } ; name } ; tony rominger ( sui ) }
for the notes records of all rows , all of them fuzzily match to high jump .
Output: all_eq { all_rows ; notes ; high jump }
the maximum primary ( 6 - 13 years ) record of all rows is 95.41 . the region record of the row with superlative primary ( 6 - 13 years ) record is o'higgins .
Output: and { eq { max { all_rows ; primary ( 6 - 13 years ) } ; 95.41 } ; eq { hop { argmax { all_rows ; primary ( 6 - 13 years ) } ; region } ; o'higgins } }
select the rows whose player record fuzzily matches to shane watson ( queensland ) . take the score record of this row . select the rows whose player record fuzzily matches to david hussey ( victoria ) . take the score record of this row . the second record is 1 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; shane watson ( queensland ) } ; score } ; hop { filter_eq { all_rows ; player ; david hussey ( victoria ) } ; score } } ; -1 }
select the rows whose date record fuzzily matches to sunday . select the row whose week record of these rows is 6th minimum . the opponent record of this row is new york giants .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; date ; sunday } ; week ; 6 } ; opponent } ; new york giants }
select the row whose years for grizzlies record of all rows is 3rd minimum . the player record of this row is bobby jackson .
Output: eq { hop { nth_argmin { all_rows ; years for grizzlies ; 3 } ; player } ; bobby jackson }
select the rows whose language record fuzzily matches to spanish . take the padilla municipality record of this row . select the rows whose language record fuzzily matches to quechua . take the padilla municipality record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; language ; spanish } ; padilla municipality } ; hop { filter_eq { all_rows ; language ; quechua } ; padilla municipality } }
the sum of the attendance record of all rows is 471,075 .
Output: round_eq { sum { all_rows ; attendance } ; 471,075 }
the average of the other details record of all rows is 2250 .
Output: round_eq { avg { all_rows ; other details } ; 2250 }
the average of the apogee record of all rows is 645 .
Output: round_eq { avg { all_rows ; apogee } ; 645 }
select the rows whose tournaments played record is equal to 19 . there is only one such row in the table . the year record of this unqiue row is 2010 .
Output: and { only { filter_eq { all_rows ; tournaments played ; 19 } } ; eq { hop { filter_eq { all_rows ; tournaments played ; 19 } ; year } ; 2010 } }
select the rows whose rank record fuzzily matches to 1 . take the bronze record of this row . select the rows whose rank record fuzzily matches to 4 . take the bronze record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; rank ; 1 } ; bronze } ; hop { filter_eq { all_rows ; rank ; 4 } ; bronze } }
select the rows whose hare quota record is greater than 10000 . there is only one such row in the table . the year record of this unqiue row is 2005 .
Output: and { only { filter_greater { all_rows ; hare quota ; 10000 } } ; eq { hop { filter_greater { all_rows ; hare quota ; 10000 } ; year } ; 2005 } }
for the type records of all rows , all of them fuzzily match to private .
Output: all_eq { all_rows ; type ; private }
select the row whose length record of all rows is maximum . the name ( year commissioned ) record of this row is dampier to bunbury natural gas pipeline ( 1984 ) .
Output: eq { hop { argmax { all_rows ; length } ; name ( year commissioned ) } ; dampier to bunbury natural gas pipeline ( 1984 ) }
select the rows whose party record fuzzily matches to democratic . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; party ; democratic } } ; 2 }
select the rows whose stadium record fuzzily matches to eden gardens . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; stadium ; eden gardens } } ; 2 }
for the location records of all rows , most of them fuzzily match to three rivers stadium .
Output: most_eq { all_rows ; location ; three rivers stadium }
the maximum best finish record of all rows is t - 17 . the year record of the row with superlative best finish record is 2000 .
Output: and { eq { max { all_rows ; best finish } ; t - 17 } ; eq { hop { argmax { all_rows ; best finish } ; year } ; 2000 } }
select the row whose founded record of all rows is 2nd maximum . the school record of this row is coastal carolina university .
Output: eq { hop { nth_argmax { all_rows ; founded ; 2 } ; school } ; coastal carolina university }
select the rows whose date record is greater than 2007 . for the competition records of these rows , most of them fuzzily match to friendly .
Output: most_eq { filter_greater { all_rows ; date ; 2007 } ; competition ; friendly }
the average of the apparent magnitude record of all rows is 10.95 .
Output: round_eq { avg { all_rows ; apparent magnitude } ; 10.95 }
select the row whose no record of all rows is 4th maximum . the player record of this row is corliss williamson .
Output: eq { hop { nth_argmax { all_rows ; no ; 4 } ; player } ; corliss williamson }
select the row whose purse record of all rows is maximum . the tournament record of this row is peter jackson champions .
Output: eq { hop { argmax { all_rows ; purse } ; tournament } ; peter jackson champions }
the maximum points record of all rows is 51 . the club record of the row with superlative points record is rodiklis kaunas .
Output: and { eq { max { all_rows ; points } ; 51 } ; eq { hop { argmax { all_rows ; points } ; club } ; rodiklis kaunas } }
select the rows whose ends won record is less than 50 . the 1st maximum stolen ends record of these rows is 14 . the locale record of the row with 1st maximum stolen ends record is germany .
Output: and { eq { nth_max { filter_less { all_rows ; ends won ; 50 } ; stolen ends ; 1 } ; 14 } ; eq { hop { nth_argmax { filter_less { all_rows ; ends won ; 50 } ; stolen ends ; 1 } ; locale } ; germany } }
select the rows whose continent record fuzzily matches to asia . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; continent ; asia } } ; 2 }
select the rows whose sentence record fuzzily matches to fine . there is only one such row in the table . the name record of this unqiue row is boris picano - nacci .
Output: and { only { filter_eq { all_rows ; sentence ; fine } } ; eq { hop { filter_eq { all_rows ; sentence ; fine } ; name } ; boris picano - nacci } }
the average of the duration record of all rows is 54 .
Output: round_eq { avg { all_rows ; duration } ; 54 }
select the rows whose date record fuzzily matches to august . for the result records of these rows , most of them fuzzily match to l .
Output: most_eq { filter_eq { all_rows ; date ; august } ; result ; l }
select the rows whose 2nd venue record fuzzily matches to mexico city . there is only one such row in the table . the year record of this unqiue row is 2007 .
Output: and { only { filter_eq { all_rows ; 2nd venue ; mexico city } } ; eq { hop { filter_eq { all_rows ; 2nd venue ; mexico city } ; year } ; 2007 } }
select the row whose stadium capacity record of all rows is 2nd maximum . the university record of this row is université laval .
Output: eq { hop { nth_argmax { all_rows ; stadium capacity ; 2 } ; university } ; université laval }
select the rows whose club record fuzzily matches to swansea uplands rfc . take the tries for record of this row . select the rows whose club record fuzzily matches to trebanos rfc . take the tries for record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; club ; swansea uplands rfc } ; tries for } ; hop { filter_eq { all_rows ; club ; trebanos rfc } ; tries for } }
the average of the high points record of all rows is 20.8 .
Output: round_eq { avg { all_rows ; high points } ; 20.8 }
select the rows whose played record fuzzily matches to 30 . the number of such rows is 16 .
Output: eq { count { filter_eq { all_rows ; played ; 30 } } ; 16 }
select the row whose rank record of all rows is minimum . the year record of this row is 2012 .
Output: eq { hop { argmin { all_rows ; rank } ; year } ; 2012 }
select the rows whose place record fuzzily matches to czechoslovakia . among these rows , select the rows whose date record is less than 1980 . the number of such rows is 3 .
Output: eq { count { filter_less { filter_eq { all_rows ; place ; czechoslovakia } ; date ; 1980 } } ; 3 }
select the rows whose name ( english ) record fuzzily matches to alliance '90 / the greens . take the votes ( 2011 ) record of this row . select the rows whose name ( english ) record fuzzily matches to free democratic party . take the votes ( 2011 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ( english ) ; alliance '90 / the greens } ; votes ( 2011 ) } ; hop { filter_eq { all_rows ; name ( english ) ; free democratic party } ; votes ( 2011 ) } }
select the rows whose position record fuzzily matches to power forward . there is only one such row in the table . the player record of this unqiue row is pete chilcutt .
Output: and { only { filter_eq { all_rows ; position ; power forward } } ; eq { hop { filter_eq { all_rows ; position ; power forward } ; player } ; pete chilcutt } }
for the rebuilt records of all rows , most of them are less than 1930 .
Output: most_less { all_rows ; rebuilt ; 1930 }
select the rows whose dominant religion ( 2002 ) record fuzzily matches to orthodox christianity . among these rows , select the rows whose population ( 2011 ) record is less than 2100 . there is only one such row in the table . the settlement record of this unqiue row is stepanovićevo .
Output: and { only { filter_less { filter_eq { all_rows ; dominant religion ( 2002 ) ; orthodox christianity } ; population ( 2011 ) ; 2100 } } ; eq { hop { filter_less { filter_eq { all_rows ; dominant religion ( 2002 ) ; orthodox christianity } ; population ( 2011 ) ; 2100 } ; settlement } ; stepanovićevo } }
select the rows whose place record fuzzily matches to pietersburg . take the population record of this row . select the rows whose place record fuzzily matches to alldays . take the population record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; place ; pietersburg } ; population } ; hop { filter_eq { all_rows ; place ; alldays } ; population } }
select the rows whose attendance record is greater than 70000 . among these rows , select the rows whose tv time record fuzzily matches to fox . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater { all_rows ; attendance ; 70000 } ; tv time ; fox } } ; 2 }
select the rows whose months in malayalam era record fuzzily matches to chingam . take the gregorian calendar record of this row . select the rows whose months in malayalam era record fuzzily matches to tulam . take the gregorian calendar record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; months in malayalam era ; chingam } ; gregorian calendar } ; hop { filter_eq { all_rows ; months in malayalam era ; tulam } ; gregorian calendar } }
select the rows whose builder record fuzzily matches to beyer peacock & co . the average of the builder 's no record of these rows is 3442 .
Output: round_eq { avg { filter_eq { all_rows ; builder ; beyer peacock & co } ; builder 's no } ; 3442 }
select the rows whose lec sport record fuzzily matches to men 's lacrosse . there is only one such row in the table . the institution record of this unqiue row is salem state university .
Output: and { only { filter_eq { all_rows ; lec sport ; men 's lacrosse } } ; eq { hop { filter_eq { all_rows ; lec sport ; men 's lacrosse } ; institution } ; salem state university } }
select the rows whose wins record is equal to 4 . among these rows , select the rows whose draws record is less than 4 . the number of such rows is 2 .
Output: eq { count { filter_less { filter_eq { all_rows ; wins ; 4 } ; draws ; 4 } } ; 2 }
select the rows whose winner record fuzzily matches to luke youlden . there is only one such row in the table . the race title record of this unqiue row is mallala .
Output: and { only { filter_eq { all_rows ; winner ; luke youlden } } ; eq { hop { filter_eq { all_rows ; winner ; luke youlden } ; race title } ; mallala } }
the maximum score record of all rows is 7 - 1 . the home team record of the row with superlative score record is aston villa .
Output: and { eq { max { all_rows ; score } ; 7 - 1 } ; eq { hop { argmax { all_rows ; score } ; home team } ; aston villa } }
select the rows whose skip record fuzzily matches to cathy king . there is only one such row in the table .
Output: only { filter_eq { all_rows ; skip ; cathy king } }
the average of the lost record of all rows is 9 .
Output: round_eq { avg { all_rows ; lost } ; 9 }
select the row whose founded record of all rows is 2nd maximum . the institution record of this row is high point university .
Output: eq { hop { nth_argmax { all_rows ; founded ; 2 } ; institution } ; high point university }
select the rows whose name record fuzzily matches to vilson ahmeti . take the term start record of this row . select the rows whose name record fuzzily matches to ilir meta . take the term start record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; vilson ahmeti } ; term start } ; hop { filter_eq { all_rows ; name ; ilir meta } ; term start } }
the average of the rank record of all rows is 11.53 .
Output: round_eq { avg { all_rows ; rank } ; 11.53 }
select the rows whose label record fuzzily matches to reveal records . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; label ; reveal records } } ; 3 }
select the rows whose 2nd component record fuzzily matches to acetone . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; 2nd component ; acetone } } ; 3 }
select the rows whose opponent record fuzzily matches to st louis cardinals . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; opponent ; st louis cardinals } } ; 2 }
select the rows whose competition record does not match to friendly . there is only one such row in the table .
Output: only { filter_not_eq { all_rows ; competition ; friendly } }
select the rows whose city of license record fuzzily matches to santa elena . there is only one such row in the table . the brand record of this unqiue row is la poderosa .
Output: and { only { filter_eq { all_rows ; city of license ; santa elena } } ; eq { hop { filter_eq { all_rows ; city of license ; santa elena } ; brand } ; la poderosa } }
select the rows whose nationality record fuzzily matches to canada . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; nationality ; canada } } ; 4 }
select the row whose number record of all rows is maximum . the unit numbers record of this row is 254001 - 254032 .
Output: eq { hop { argmax { all_rows ; number } ; unit numbers } ; 254001 - 254032 }
select the rows whose institution record fuzzily matches to lawrence technological university . take the enrollment record of this row . select the rows whose institution record fuzzily matches to northern michigan university . take the enrollment record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; institution ; lawrence technological university } ; enrollment } ; hop { filter_eq { all_rows ; institution ; northern michigan university } ; enrollment } }
select the row whose points ( pts ) record of all rows is 2nd maximum . the team ( equipo ) record of this row is san francisco fc .
Output: eq { hop { nth_argmax { all_rows ; points ( pts ) ; 2 } ; team ( equipo ) } ; san francisco fc }
the sum of the wins record of all rows is 4 .
Output: round_eq { sum { all_rows ; wins } ; 4 }
select the rows whose municipality record fuzzily matches to tecate . take the area ( km2 ) record of this row . select the rows whose municipality record fuzzily matches to tijuana . take the area ( km2 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; municipality ; tecate } ; area ( km2 ) } ; hop { filter_eq { all_rows ; municipality ; tijuana } ; area ( km2 ) } }
select the rows whose country record fuzzily matches to russia . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; country ; russia } } ; 2 }
for the venue records of all rows , all of them fuzzily match to national stadium , tokyo , tokyo .
Output: all_eq { all_rows ; venue ; national stadium , tokyo , tokyo }
the average of the year erected record of all rows is 1965 .
Output: round_eq { avg { all_rows ; year erected } ; 1965 }
the average of the viewing figure record of all rows is 6.40 million .
Output: round_eq { avg { all_rows ; viewing figure } ; 6.40 million }
for the attendance records of all rows , most of them are less than 17000 .
Output: most_less { all_rows ; attendance ; 17000 }
select the rows whose written by record fuzzily matches to john sullivan . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; written by ; john sullivan } } ; 4 }
select the rows whose rank record is equal to 12 . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; rank ; 12 } } ; 6 }
select the rows whose score record is greater than 400 . there is only one such row in the table . the batsmen record of this unqiue row is adrian dale and viv richards .
Output: and { only { filter_greater { all_rows ; score ; 400 } } ; eq { hop { filter_greater { all_rows ; score ; 400 } ; batsmen } ; adrian dale and viv richards } }
select the rows whose incoming manager record fuzzily matches to darío franco . take the date hired record of this row . select the rows whose incoming manager record fuzzily matches to octavio becerril . take the date hired record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incoming manager ; darío franco } ; date hired } ; hop { filter_eq { all_rows ; incoming manager ; octavio becerril } ; date hired } }
the average of the races record of all rows is 16.6 .
Output: round_eq { avg { all_rows ; races } ; 16.6 }
select the rows whose college / junior / club team record fuzzily matches to calgary centennials . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; college / junior / club team ; calgary centennials } } ; 2 }
select the rows whose shuji kondo record fuzzily matches to kondo ( 0:33 ) . there is only one such row in the table . the block a record of this unqiue row is ryuji hijikata .
Output: and { only { filter_eq { all_rows ; shuji kondo ; kondo ( 0:33 ) } } ; eq { hop { filter_eq { all_rows ; shuji kondo ; kondo ( 0:33 ) } ; block a } ; ryuji hijikata } }
select the row whose number of seasons in top division record of all rows is 3rd maximum . the club record of this row is rijeka a , b .
Output: eq { hop { nth_argmax { all_rows ; number of seasons in top division ; 3 } ; club } ; rijeka a , b }
select the rows whose date record fuzzily matches to may 27 . take the high rebounds record of this row . select the rows whose date record fuzzily matches to may 29 . take the high rebounds record of this row . the first record is 2 larger than the second record . the high rebounds record of the first row is duncan ( 17 ) . the high rebounds record of the second row is duncan ( 15 ) .
Output: and { eq { diff { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } } ; 2 } ; and { eq { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; duncan ( 17 ) } ; eq { hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } ; duncan ( 15 ) } } }
select the row whose points against record of all rows is maximum . the club record of this row is cwmgors rfc .
Output: eq { hop { argmax { all_rows ; points against } ; club } ; cwmgors rfc }
select the row whose date record of all rows is 9th minimum . the race record of this row is italian grand prix .
Output: eq { hop { nth_argmin { all_rows ; date ; 9 } ; race } ; italian grand prix }
for the date records of all rows , most of them fuzzily match to 25 october .
Output: most_eq { all_rows ; date ; 25 october }
for the date records of all rows , all of them fuzzily match to 21 july 1951 .
Output: all_eq { all_rows ; date ; 21 july 1951 }
select the rows whose namesake record fuzzily matches to assyrian town where ishtar was worshipped . there is only one such row in the table . the name record of this unqiue row is arbela sulcus .
Output: and { only { filter_eq { all_rows ; namesake ; assyrian town where ishtar was worshipped } } ; eq { hop { filter_eq { all_rows ; namesake ; assyrian town where ishtar was worshipped } ; name } ; arbela sulcus } }
select the rows whose region record fuzzily matches to united kingdom . take the date record of this row . select the rows whose region record fuzzily matches to japan . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; region ; united kingdom } ; date } ; hop { filter_eq { all_rows ; region ; japan } ; date } }
select the rows whose laps record is less than 17 . there is only one such row in the table . the year record of this unqiue row is 1966 .
Output: and { only { filter_less { all_rows ; laps ; 17 } } ; eq { hop { filter_less { all_rows ; laps ; 17 } ; year } ; 1966 } }
select the row whose density square record of all rows is 1st maximum . the municipality record of this row is malmö .
Output: eq { hop { nth_argmax { all_rows ; density square ; 1 } ; municipality } ; malmö }
select the rows whose outcome record fuzzily matches to runner - up . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; outcome ; runner - up } } ; 6 }
select the rows whose language record fuzzily matches to english . the sum of the points record of these rows is 43 .
Output: round_eq { sum { filter_eq { all_rows ; language ; english } ; points } ; 43 }
the average of the margin of victory record of all rows is 2 .
Output: round_eq { avg { all_rows ; margin of victory } ; 2 }
select the rows whose film record fuzzily matches to chicago . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; film ; chicago } } ; 2 }
the average of the elevation ( m ) record of all rows is 4390 .
Output: round_eq { avg { all_rows ; elevation ( m ) } ; 4390 }
select the rows whose to par record is equal to 11 . there is only one such row in the table . the player record of this unqiue row is tiger woods .
Output: and { only { filter_eq { all_rows ; to par ; 11 } } ; eq { hop { filter_eq { all_rows ; to par ; 11 } ; player } ; tiger woods } }
select the row whose time record of all rows is 2nd minimum . the name record of this row is pieter van den hoogenband .
Output: eq { hop { nth_argmin { all_rows ; time ; 2 } ; name } ; pieter van den hoogenband }
select the rows whose result record fuzzily matches to win . for the orangemen points records of these rows , most of them are greater than 20 .
Output: most_greater { filter_eq { all_rows ; result ; win } ; orangemen points ; 20 }
for the pl gp records of all rows , all of them are equal to 0 .
Output: all_eq { all_rows ; pl gp ; 0 }
select the row whose us viewers ( million ) record of all rows is maximum . the title record of this row is identity crisis .
Output: eq { hop { argmax { all_rows ; us viewers ( million ) } ; title } ; identity crisis }
select the row whose attendance record of all rows is 3rd maximum . the date record of this row is november 27 , 1988 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; date } ; november 27 , 1988 }
select the rows whose number of electorates ( 2009 ) record is greater than 190000 . there is only one such row in the table . the name record of this unqiue row is suwasra .
Output: and { only { filter_greater { all_rows ; number of electorates ( 2009 ) ; 190000 } } ; eq { hop { filter_greater { all_rows ; number of electorates ( 2009 ) ; 190000 } ; name } ; suwasra } }
for the first aired records of all rows , most of them are greater than 1970 .
Output: most_greater { all_rows ; first aired ; 1970 }
the sum of the crowd record of all rows is 149299 .
Output: round_eq { sum { all_rows ; crowd } ; 149299 }
select the row whose us viewers ( millions ) record of all rows is maximum . the no in series record of this row is 8 .
Output: eq { hop { argmax { all_rows ; us viewers ( millions ) } ; no in series } ; 8 }
select the rows whose arena record fuzzily matches to jobingcom arena . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; arena ; jobingcom arena } } ; 2 }
select the rows whose surface record fuzzily matches to carpet ( i ) . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; surface ; carpet ( i ) } } ; 2 }
select the rows whose venue record fuzzily matches to italy . there is only one such row in the table . the date record of this unqiue row is june 10 , 1990 .
Output: and { only { filter_eq { all_rows ; venue ; italy } } ; eq { hop { filter_eq { all_rows ; venue ; italy } ; date } ; june 10 , 1990 } }
select the rows whose womens singles record fuzzily matches to zhou mi . there is only one such row in the table .
Output: only { filter_eq { all_rows ; womens singles ; zhou mi } }
select the row whose height record of all rows is 3rd maximum . the player record of this row is caner pekşen .
Output: eq { hop { nth_argmax { all_rows ; height ; 3 } ; player } ; caner pekşen }
select the rows whose nationality record fuzzily matches to usa . among these rows , select the rows whose position record fuzzily matches to rw . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; nationality ; usa } ; position ; rw } } ; 2 }
select the rows whose state record fuzzily matches to oregon . select the row whose year record of these rows is 2nd minimum . the result record of this row is resigned .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; state ; oregon } ; year ; 2 } ; result } ; resigned }
select the row whose size record of all rows is 2nd maximum . the building record of this row is copenhagen concert hall .
Output: eq { hop { nth_argmax { all_rows ; size ; 2 } ; building } ; copenhagen concert hall }
select the rows whose title record fuzzily matches to face / off . take the year record of this row . select the rows whose title record fuzzily matches to antz . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; title ; face / off } ; year } ; hop { filter_eq { all_rows ; title ; antz } ; year } }
select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60,000 . the number of such rows is 1 .
Output: eq { count { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60,000 } } ; 1 }
select the row whose money record of all rows is maximum . the place record of this row is 1 .
Output: eq { hop { argmax { all_rows ; money } ; place } ; 1 }
select the row whose earnings record of all rows is 3rd maximum . the player record of this row is arnold palmer .
Output: eq { hop { nth_argmax { all_rows ; earnings ; 3 } ; player } ; arnold palmer }
select the rows whose directed by record fuzzily matches to steve buscemi . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; directed by ; steve buscemi } } ; 2 }
select the row whose qual 1 record of all rows is 2nd minimum . the name record of this row is sébastien bourdais .
Output: eq { hop { nth_argmin { all_rows ; qual 1 ; 2 } ; name } ; sébastien bourdais }
the average of the quantity made record of all rows is 3.83 .
Output: round_eq { avg { all_rows ; quantity made } ; 3.83 }
select the row whose runs record of all rows is 2nd maximum . the fielding team record of this row is india .
Output: eq { hop { nth_argmax { all_rows ; runs ; 2 } ; fielding team } ; india }
select the row whose enrollment record of all rows is maximum . the institution record of this row is eastern university .
Output: eq { hop { argmax { all_rows ; enrollment } ; institution } ; eastern university }
for the crowd records of all rows , most of them are greater than or equal to 10000 .
Output: most_greater_eq { all_rows ; crowd ; 10000 }
select the rows whose opposing teams record fuzzily matches to scotland . take the against record of this row . select the rows whose opposing teams record fuzzily matches to wales . take the against record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opposing teams ; scotland } ; against } ; hop { filter_eq { all_rows ; opposing teams ; wales } ; against } }
select the row whose floors record of all rows is maximum . the name record of this row is place hauteville .
Output: eq { hop { argmax { all_rows ; floors } ; name } ; place hauteville }
select the rows whose school / club team record fuzzily matches to villanova . there is only one such row in the table . the player record of this unqiue row is michael bradley .
Output: and { only { filter_eq { all_rows ; school / club team ; villanova } } ; eq { hop { filter_eq { all_rows ; school / club team ; villanova } ; player } ; michael bradley } }
select the rows whose written by record fuzzily matches to julian jones . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; written by ; julian jones } } ; 6 }
select the row whose gold record of all rows is 2nd maximum . the nation record of this row is romania .
Output: eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; romania }
select the row whose score record of all rows is maximum . the team 1 record of this row is fc nantes ( d1 ) .
Output: eq { hop { argmax { all_rows ; score } ; team 1 } ; fc nantes ( d1 ) }
for the wins records of all rows , most of them are less than 15 .
Output: most_less { all_rows ; wins ; 15 }
select the row whose time record of all rows is 2nd minimum . the winner record of this row is paddy o'prado .
Output: eq { hop { nth_argmin { all_rows ; time ; 2 } ; winner } ; paddy o'prado }
select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 0 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 0 } } ; 2 }
the average of the total receipts record of all rows is 12444351 .
Output: round_eq { avg { all_rows ; total receipts } ; 12444351 }
the average of the attendance record of all rows is 58260 .
Output: round_eq { avg { all_rows ; attendance } ; 58260 }
select the rows whose home team score record is greater than 9.0 . among these rows , select the rows whose crowd record is greater than 10000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_greater { all_rows ; home team score ; 9.0 } ; crowd ; 10000 } } ; 2 }
select the rows whose language record fuzzily matches to english . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; language ; english } } ; 3 }
select the rows whose date record fuzzily matches to february . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; date ; february } } ; 4 }
select the rows whose high rebounds record fuzzily matches to catchings . for the high points records of these rows , most of them fuzzily match to catchings .
Output: most_eq { filter_eq { all_rows ; high rebounds ; catchings } ; high points ; catchings }
select the rows whose congress record fuzzily matches to 111th congress . among these rows , select the rows whose date introduced record fuzzily matches to april . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; congress ; 111th congress } ; date introduced ; april } } ; 2 }
select the rows whose song record fuzzily matches to retour . take the place record of this row . select the rows whose song record fuzzily matches to waterman . take the place record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; song ; retour } ; place } ; hop { filter_eq { all_rows ; song ; waterman } ; place } }
select the rows whose name record fuzzily matches to john e wool . take the appointment date record of this row . select the rows whose name record fuzzily matches to david e twiggs . take the appointment date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; john e wool } ; appointment date } ; hop { filter_eq { all_rows ; name ; david e twiggs } ; appointment date } }
select the rows whose incumbent record fuzzily matches to john kline . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to jim ramstad . take the first elected record of this row . the first record is greater than the second record . the first elected record of the first row is 2002 . the first elected record of the second row is 1990 .
Output: and { greater { hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected } } ; and { eq { hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected } ; 2002 } ; eq { hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected } ; 1990 } } }
select the rows whose surface record fuzzily matches to clay . among these rows , select the rows whose week record fuzzily matches to may . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; surface ; clay } ; week ; may } } ; 2 }
the sum of the pubs 2010 record of all rows is 457 .
Output: round_eq { sum { all_rows ; pubs 2010 } ; 457 }
select the rows whose date record fuzzily matches to 1989 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; date ; 1989 } } ; 4 }
select the rows whose roads intersected record fuzzily matches to route 53 . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; roads intersected ; route 53 } } ; 1 }
select the rows whose year record fuzzily matches to 1967 . take the laps record of this row . select the rows whose year record fuzzily matches to 1971 . take the laps record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1967 } ; laps } ; hop { filter_eq { all_rows ; year ; 1971 } ; laps } }
select the rows whose surface record fuzzily matches to carpet . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; surface ; carpet } } ; 2 }
for the subregion records of all rows , most of them fuzzily match to baixo vouga .
Output: most_eq { all_rows ; subregion ; baixo vouga }
select the rows whose average attendance as % of capacity record is greater than or equal to 90 . the number of such rows is 5 .
Output: eq { count { filter_greater_eq { all_rows ; average attendance as % of capacity ; 90 } } ; 5 }
select the row whose 2011 ( imf ) record of all rows is 3rd maximum . the nation record of this row is uruguay .
Output: eq { hop { nth_argmax { all_rows ; 2011 ( imf ) ; 3 } ; nation } ; uruguay }
select the rows whose singer record fuzzily matches to samanta karavello . take the points record of this row . select the rows whose singer record fuzzily matches to teuta kurti . take the points record of this row . the first record is 20 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; singer ; samanta karavello } ; points } ; hop { filter_eq { all_rows ; singer ; teuta kurti } ; points } } ; 20 }
select the rows whose spectators record is equal to 50000 . among these rows , select the rows whose res record fuzzily matches to 3 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; spectators ; 50000 } ; res ; 3 } } ; 2 }
for the dominant religion ( 2002 ) records of all rows , all of them fuzzily match to orthodox christianity .
Output: all_eq { all_rows ; dominant religion ( 2002 ) ; orthodox christianity }
select the row whose attendance record of all rows is maximum . the date record of this row is 17 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; 17 }
select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is ralph a gamble .
Output: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; ralph a gamble }
for the final - rank records of all rows , most of them are less than or equal to 5 .
Output: most_less_eq { all_rows ; final - rank ; 5 }
select the rows whose result record fuzzily matches to 2nd . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; result ; 2nd } } ; 2 }
select the row whose total record of all rows is maximum . the nation record of this row is australia .
Output: eq { hop { argmax { all_rows ; total } ; nation } ; australia }
select the row whose distance record of all rows is minimum . the city 1 record of this row is são paulo . the city 2 record of this row is rio de janeiro .
Output: and { eq { hop { argmin { all_rows ; distance } ; city 1 } ; são paulo } ; eq { hop { argmin { all_rows ; distance } ; city 2 } ; rio de janeiro } }
select the rows whose written by record fuzzily matches to chip johannessen . there is only one such row in the table . the season record of this unqiue row is 17 .
Output: and { only { filter_eq { all_rows ; written by ; chip johannessen } } ; eq { hop { filter_eq { all_rows ; written by ; chip johannessen } ; season } ; 17 } }
select the rows whose city record fuzzily matches to barisal . take the country record of this row . select the rows whose city record fuzzily matches to chittagong . take the country record of this row . the first record fuzzily matches to the second record . the country record of the first row is bangladesh . the country record of the second row is bangladesh .
Output: and { eq { hop { filter_eq { all_rows ; city ; barisal } ; country } ; hop { filter_eq { all_rows ; city ; chittagong } ; country } } ; and { eq { hop { filter_eq { all_rows ; city ; barisal } ; country } ; bangladesh } ; eq { hop { filter_eq { all_rows ; city ; chittagong } ; country } ; bangladesh } } }
select the rows whose home team score record is less than 10 . the average of the crowd record of these rows is 16600 .
Output: round_eq { avg { filter_less { all_rows ; home team score ; 10 } ; crowd } ; 16600 }
select the rows whose locomotive type record fuzzily matches to steam . select the row whose withdrawn record of these rows is 1st minimum . the number record of this row is 1 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; locomotive type ; steam } ; withdrawn ; 1 } ; number } ; 1 }
select the rows whose position record fuzzily matches to left wing . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; position ; left wing } } ; 4 }
for the directed by records of all rows , all of them fuzzily match to tyler perry .
Output: all_eq { all_rows ; directed by ; tyler perry }
the average of the round record of all rows is 3.7 .
Output: round_eq { avg { all_rows ; round } ; 3.7 }
select the rows whose time record is equal to 25.61 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; time ; 25.61 } } ; 2 }
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 .
Output: 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 } } }
select the rows whose viewers ( millions ) record is less than 4 million . there is only one such row in the table .
Output: only { filter_less { all_rows ; viewers ( millions ) ; 4 million } }