niryuu/sni-each-converted huggingface dataset:
select the rows whose result record fuzzily matches to re-elected . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; result ; re-elected } } ; 6 }
select the rows whose partner record fuzzily matches to tom nijssen . there is only one such row in the table . the year record of this unqiue row is 1992 . the championship record of this unqiue row is us open .
Output: and { only { filter_eq { all_rows ; partner ; tom nijssen } } ; and { eq { hop { filter_eq { all_rows ; partner ; tom nijssen } ; year } ; 1992 } ; eq { hop { filter_eq { all_rows ; partner ; tom nijssen } ; championship } ; us open } } }
select the rows whose team record fuzzily matches to bms scuderia italia . take the laps record of this row . select the rows whose team record fuzzily matches to jb racing . take the laps record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team ; bms scuderia italia } ; laps } ; hop { filter_eq { all_rows ; team ; jb racing } ; laps } }
for the wins records of all rows , most of them are greater than or equal to 1 .
Output: most_greater_eq { all_rows ; wins ; 1 }
select the rows whose partner record fuzzily matches to sergio galdós . there is only one such row in the table . the tournament record of this unqiue row is panama city .
Output: and { only { filter_eq { all_rows ; partner ; sergio galdós } } ; eq { hop { filter_eq { all_rows ; partner ; sergio galdós } ; tournament } ; panama city } }
select the rows whose site record fuzzily matches to cedar rapids . there is only one such row in the table . the date record of this unqiue row is september 4 , 2007 .
Output: and { only { filter_eq { all_rows ; site ; cedar rapids } } ; eq { hop { filter_eq { all_rows ; site ; cedar rapids } ; date } ; september 4 , 2007 } }
select the rows whose party affiliation record fuzzily matches to dravida munnetra kazhagam . among these rows , select the rows whose name of mp record fuzzily matches to t r baalu . there is only one such row in the table . the duration record of this unqiue row is 2009 - incumbent .
Output: and { only { filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu } } ; eq { hop { filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu } ; duration } ; 2009 - incumbent } }
select the rows whose party record fuzzily matches to labour party . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; party ; labour party } } ; 3 }
select the row whose founded record of all rows is minimum . the club record of this row is fk vojvodina .
Output: eq { hop { argmin { all_rows ; founded } ; club } ; fk vojvodina }
select the rows whose year record is equal to 1961 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; year ; 1961 } } ; 3 }
select the row whose time record of all rows is 1st minimum . the race record of this row is maiden .
Output: eq { hop { nth_argmin { all_rows ; time ; 1 } ; race } ; maiden }
select the row whose founded record of all rows is 1st minimum . the institution record of this row is midway college 1 .
Output: eq { hop { nth_argmin { all_rows ; founded ; 1 } ; institution } ; midway college 1 }
select the rows whose northumberland senior benevolent bowl record fuzzily matches to blyth town . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; northumberland senior benevolent bowl ; blyth town } } ; 2 }
select the rows whose place record fuzzily matches to waddon . the sum of the platforms record of these rows is 4 .
Output: round_eq { sum { filter_eq { all_rows ; place ; waddon } ; platforms } ; 4 }
select the rows whose no record is arbitrary . the number of such rows is 11 .
Output: eq { count { filter_all { all_rows ; no } } ; 11 }
the average of the laps record of all rows is 164 .
Output: round_eq { avg { all_rows ; laps } ; 164 }
for the date of birth records of all rows , most of them are less than 1980 .
Output: most_less { all_rows ; date of birth ; 1980 }
select the row whose listed record of all rows is 3rd maximum . the county record of this row is lake .
Output: eq { hop { nth_argmax { all_rows ; listed ; 3 } ; county } ; lake }
the maximum average record of all rows is 25.29 . the player record of the row with superlative average record is clarrie grimmett ( vic / sa ) .
Output: and { eq { max { all_rows ; average } ; 25.29 } ; eq { hop { argmax { all_rows ; average } ; player } ; clarrie grimmett ( vic / sa ) } }
select the rows whose name record fuzzily matches to cavendish ( ex - sibyl ) . take the launched record of this row . select the rows whose name record fuzzily matches to caesar ( ex - ranger ) . take the launched record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; cavendish ( ex - sibyl ) } ; launched } ; hop { filter_eq { all_rows ; name ; caesar ( ex - ranger ) } ; launched } }
select the rows whose 1st round record fuzzily matches to 0-0 . there is only one such row in the table . the team 1 record of this unqiue row is as angoulême ( d2 ) . the team 2 record of this unqiue row is fc sochaux - montbéliard ( d1 ) .
Output: and { only { filter_eq { all_rows ; 1st round ; 0-0 } } ; and { eq { hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 1 } ; as angoulême ( d2 ) } ; eq { hop { filter_eq { all_rows ; 1st round ; 0-0 } ; team 2 } ; fc sochaux - montbéliard ( d1 ) } } }
select the rows whose date record fuzzily matches to september . for the score records of these rows , most of them fuzzily match to w .
Output: most_eq { filter_eq { all_rows ; date ; september } ; score ; w }
select the row whose points record of all rows is 2nd maximum . the team record of this row is germnia .
Output: eq { hop { nth_argmax { all_rows ; points ; 2 } ; team } ; germnia }
select the row whose frequency mhz record of all rows is minimum . the city of license record of this row is port st lucie , florida .
Output: eq { hop { argmin { all_rows ; frequency mhz } ; city of license } ; port st lucie , florida }
select the rows whose player record fuzzily matches to ben handlogten . take the years for jazz record of this row . select the rows whose player record fuzzily matches to bobby hansen . take the years for jazz record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; ben handlogten } ; years for jazz } ; hop { filter_eq { all_rows ; player ; bobby hansen } ; years for jazz } }
the average of the gold record of all rows is .667 .
Output: round_eq { avg { all_rows ; gold } ; .667 }
select the row whose result record of all rows is 1st maximum . the incumbent record of this row is john lewis .
Output: eq { hop { nth_argmax { all_rows ; result ; 1 } ; incumbent } ; john lewis }
select the rows whose pole position record fuzzily matches to chris goodwin andrew kirkaldy . there is only one such row in the table . the round record of this unqiue row is 5 .
Output: and { only { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } } ; eq { hop { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } ; round } ; 5 } }
the average of the location attendance record of all rows is 917 .
Output: round_eq { avg { all_rows ; location attendance } ; 917 }
select the rows whose int ' l debut record fuzzily matches to france . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; int ' l debut ; france } } ; 2 }
the average of the apparent magnitude record of all rows is 12.01 .
Output: round_eq { avg { all_rows ; apparent magnitude } ; 12.01 }
select the rows whose directed by record fuzzily matches to allan arkush . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; directed by ; allan arkush } } ; 2 }
select the rows whose season record is equal to 2002 . select the row whose points record of these rows is maximum . the series record of this row is british formula three .
Output: eq { hop { argmax { filter_eq { all_rows ; season ; 2002 } ; points } ; series } ; british formula three }
select the rows whose turbines record is equal to 1 . there is only one such row in the table . the wind farm record of this unqiue row is glenough extension .
Output: and { only { filter_eq { all_rows ; turbines ; 1 } } ; eq { hop { filter_eq { all_rows ; turbines ; 1 } ; wind farm } ; glenough extension } }
the average of the points record of all rows is 57 .
Output: round_eq { avg { all_rows ; points } ; 57 }
select the rows whose high rebounds record fuzzily matches to tyrus thomas . the sum of the high rebounds record of these rows is 58 .
Output: round_eq { sum { filter_eq { all_rows ; high rebounds ; tyrus thomas } ; high rebounds } ; 58 }
select the rows whose status record fuzzily matches to six . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; status ; six } } ; 5 }
select the rows whose matches record is equal to 139 . there is only one such row in the table . the team record of this unqiue row is darlington .
Output: and { only { filter_eq { all_rows ; matches ; 139 } } ; eq { hop { filter_eq { all_rows ; matches ; 139 } ; team } ; darlington } }
for the writer records of all rows , most of them fuzzily match to bill gallagher .
Output: most_eq { all_rows ; writer ; bill gallagher }
the sum of the silver record of all rows is 27 .
Output: round_eq { sum { all_rows ; silver } ; 27 }
select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose result record fuzzily matches to lost . there is only one such row in the table . the incumbent record of this unqiue row is dan crane .
Output: and { only { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; lost } } ; eq { hop { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; lost } ; incumbent } ; dan crane } }
select the rows whose years record fuzzily matches to 192 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; years ; 192 } } ; 2 }
select the rows whose written by record fuzzily matches to adam barr . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; written by ; adam barr } } ; 3 }
the sum of the time record of all rows is 15:41 .
Output: round_eq { sum { all_rows ; time } ; 15:41 }
select the rows whose player record fuzzily matches to tom bladon . take the round record of this row . select the rows whose player record fuzzily matches to jim watson . take the round record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; tom bladon } ; round } ; hop { filter_eq { all_rows ; player ; jim watson } ; round } }
select the rows whose margin of victory record does not match to playoff . the average of the margin of victory record of these rows is 2.5 .
Output: round_eq { avg { filter_not_eq { all_rows ; margin of victory ; playoff } ; margin of victory } ; 2.5 }
select the rows whose city of license record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; city of license } } ; 5 }
select the rows whose year born record is equal to 1978 . among these rows , select the rows whose year born record is equal to 1978 . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; year born ; 1978 } ; year born ; 1978 } } ; 3 }
the sum of the wins record of all rows is 45 .
Output: round_eq { sum { all_rows ; wins } ; 45 }
select the rows whose series name record fuzzily matches to 3000 pro series . there is only one such row in the table . the season record of this unqiue row is 2005 .
Output: and { only { filter_eq { all_rows ; series name ; 3000 pro series } } ; eq { hop { filter_eq { all_rows ; series name ; 3000 pro series } ; season } ; 2005 } }
select the rows whose to par record fuzzily matches to +1 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; to par ; +1 } } ; 2 }
select the row whose result record of all rows is maximum . the date record of this row is september 29 , 2003 .
Output: eq { hop { argmax { all_rows ; result } ; date } ; september 29 , 2003 }
for the year records of all rows , most of them are less than 1960 .
Output: most_less { all_rows ; year ; 1960 }
for the wheels records of all rows , most of them fuzzily match to 0-4 .
Output: most_eq { all_rows ; wheels ; 0-4 }
select the row whose vote percentage record of all rows is 2nd maximum . the party record of this row is social democratic party ( spd ) .
Output: eq { hop { nth_argmax { all_rows ; vote percentage ; 2 } ; party } ; social democratic party ( spd ) }
select the rows whose colour record fuzzily matches to brown . there is only one such row in the table . the name record of this unqiue row is ted .
Output: and { only { filter_eq { all_rows ; colour ; brown } } ; eq { hop { filter_eq { all_rows ; colour ; brown } ; name } ; ted } }
select the row whose points record of all rows is 2nd maximum . the artist record of this row is sirusho .
Output: eq { hop { nth_argmax { all_rows ; points ; 2 } ; artist } ; sirusho }
for the certification records of all rows , most of them fuzzily match to platinum .
Output: most_eq { all_rows ; certification ; platinum }
select the rows whose tournament record fuzzily matches to australian international . take the year record of this row . select the rows whose tournament record fuzzily matches to canadian open . take the year record of this row . the first record is 1 year larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; tournament ; australian international } ; year } ; hop { filter_eq { all_rows ; tournament ; canadian open } ; year } } ; 1 year }
select the rows whose under - 13 record fuzzily matches to emily park . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; under - 13 ; emily park } } ; 2 }
select the row whose transfer fee record of all rows is maximum . the name record of this row is becchio .
Output: eq { hop { argmax { all_rows ; transfer fee } ; name } ; becchio }
for the transfer fee ( millions ) records of all rows , most of them are greater than 26 .
Output: most_greater { all_rows ; transfer fee ( millions ) ; 26 }
the average of the voltage record of all rows is 1.75 .
Output: round_eq { avg { all_rows ; voltage } ; 1.75 }
for the attendance records of all rows , most of them are greater than 70000 .
Output: most_greater { all_rows ; attendance ; 70000 }
select the rows whose tournament record fuzzily matches to omega european masters . take the margin of victory record of this row . select the rows whose tournament record fuzzily matches to madrid masters . 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 ; tournament ; omega european masters } ; margin of victory } ; hop { filter_eq { all_rows ; tournament ; madrid masters } ; margin of victory } }
select the rows whose city record fuzzily matches to newcastle . take the erp ( analog / digital ) record of this row . select the rows whose city record fuzzily matches to lismore . take the erp ( analog / digital ) record of this row . the first record is greater than the second record . the erp ( analog / digital ) record of the first row is 1200 kw 500 kw . the erp ( analog / digital ) record of the second row is 200 kw 200 kw .
Output: and { greater { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } } ; and { eq { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; 1200 kw 500 kw } ; eq { hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } ; 200 kw 200 kw } } }
the sum of the total produced record of all rows is 229 .
Output: round_eq { sum { all_rows ; total produced } ; 229 }
select the row whose score record of all rows is maximum . the opponent record of this row is toronto maple leafs .
Output: eq { hop { argmax { all_rows ; score } ; opponent } ; toronto maple leafs }
select the rows whose men 's singles record fuzzily matches to nicholas hall . take the year record of this row . select the rows whose men 's singles record fuzzily matches to andrew smith . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; men 's singles ; nicholas hall } ; year } ; hop { filter_eq { all_rows ; men 's singles ; andrew smith } ; year } }
the average of the area ( km square ) record of all rows is 110,129 .
Output: round_eq { avg { all_rows ; area ( km square ) } ; 110,129 }
the average of the tournaments record of all rows is 11.46 .
Output: round_eq { avg { all_rows ; tournaments } ; 11.46 }
select the rows whose capacity record is greater than 84000 . there is only one such row in the table . the stadium record of this unqiue row is soccer city .
Output: and { only { filter_greater { all_rows ; capacity ; 84000 } } ; eq { hop { filter_greater { all_rows ; capacity ; 84000 } ; stadium } ; soccer city } }
for the high rebounds records of all rows , most of them fuzzily match to chris bosh .
Output: most_eq { all_rows ; high rebounds ; chris bosh }
select the rows whose erp w record is equal to 8 . there is only one such row in the table . the call sign record of this unqiue row is k251ah .
Output: and { only { filter_eq { all_rows ; erp w ; 8 } } ; eq { hop { filter_eq { all_rows ; erp w ; 8 } ; call sign } ; k251ah } }
select the rows whose name record fuzzily matches to sally foster . take the time record of this row . select the rows whose name record fuzzily matches to anne poleska . take the time record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; sally foster } ; time } ; hop { filter_eq { all_rows ; name ; anne poleska } ; time } }
select the rows whose opponent record fuzzily matches to coyotes . the sum of the attendance record of these rows is 26,689 .
Output: round_eq { sum { filter_eq { all_rows ; opponent ; coyotes } ; attendance } ; 26,689 }
select the row whose pick record of all rows is 2nd maximum . the player record of this row is jason clemett .
Output: eq { hop { nth_argmax { all_rows ; pick ; 2 } ; player } ; jason clemett }
select the row whose round record of all rows is minimum . the name record of this row is sean weatherspoon .
Output: eq { hop { argmin { all_rows ; round } ; name } ; sean weatherspoon }
select the rows whose year record fuzzily matches to 1975 . take the stages record of this row . select the rows whose year record fuzzily matches to 1976 . take the stages record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1975 } ; stages } ; hop { filter_eq { all_rows ; year ; 1976 } ; stages } }
select the rows whose ihsaa class record fuzzily matches to aa . for the county records of these rows , most of them fuzzily match to 28 greene .
Output: most_eq { filter_eq { all_rows ; ihsaa class ; aa } ; county ; 28 greene }
select the rows whose cuts made record is equal to 3 . there is only one such row in the table . the tournament record of this unqiue row is us open .
Output: and { only { filter_eq { all_rows ; cuts made ; 3 } } ; eq { hop { filter_eq { all_rows ; cuts made ; 3 } ; tournament } ; us open } }
the average of the pick record of all rows is 16.6 .
Output: round_eq { avg { all_rows ; pick } ; 16.6 }
select the rows whose segment b record fuzzily matches to cheesecake . there is only one such row in the table . the series ep record of this unqiue row is 9 - 01 .
Output: and { only { filter_eq { all_rows ; segment b ; cheesecake } } ; eq { hop { filter_eq { all_rows ; segment b ; cheesecake } ; series ep } ; 9 - 01 } }
the average of the capacity record of all rows is 8561 .
Output: round_eq { avg { all_rows ; capacity } ; 8561 }
the average of the weight ( lb ) record of all rows is 212.14 .
Output: round_eq { avg { all_rows ; weight ( lb ) } ; 212.14 }
select the rows whose location record fuzzily matches to grodno . there is only one such row in the table . the team record of this unqiue row is neman .
Output: and { only { filter_eq { all_rows ; location ; grodno } } ; eq { hop { filter_eq { all_rows ; location ; grodno } ; team } ; neman } }
select the rows whose player record fuzzily matches to tom watson . take the earnings record of this row . select the rows whose player record fuzzily matches to lee trevino . take the earnings record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; tom watson } ; earnings } ; hop { filter_eq { all_rows ; player ; lee trevino } ; earnings } }
the average of the enrollment record of all rows is 1133 .
Output: round_eq { avg { all_rows ; enrollment } ; 1133 }
select the rows whose school record fuzzily matches to merrillville . take the year joined record of this row . select the rows whose school record fuzzily matches to laporte . take the year joined record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; school ; merrillville } ; year joined } ; hop { filter_eq { all_rows ; school ; laporte } ; year joined } }
select the row whose championship record of all rows is 2nd maximum . the name record of this row is robert page .
Output: eq { hop { nth_argmax { all_rows ; championship ; 2 } ; name } ; robert page }
select the rows whose directed by record fuzzily matches to jeff bleckner . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; directed by ; jeff bleckner } } ; 3 }
select the rows whose home team score record is less than 15 . among these rows , select the rows whose crowd record is greater than 15000 . the number of such rows is 3 .
Output: eq { count { filter_greater { filter_less { all_rows ; home team score ; 15 } ; crowd ; 15000 } } ; 3 }
the average of the attendance record of all rows is 26593 .
Output: round_eq { avg { all_rows ; attendance } ; 26593 }
select the rows whose county record fuzzily matches to new haven . for the proposed records of these rows , most of them are less than 2000 .
Output: most_less { filter_eq { all_rows ; county ; new haven } ; proposed ; 2000 }
select the rows whose round record fuzzily matches to group h . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; round ; group h } } ; 3 }
select the rows whose start record fuzzily matches to aurillac . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; start ; aurillac } } ; 5 }
select the rows whose playoffs record fuzzily matches to league finals . there is only one such row in the table . the year record of this unqiue row is 1978 .
Output: and { only { filter_eq { all_rows ; playoffs ; league finals } } ; eq { hop { filter_eq { all_rows ; playoffs ; league finals } ; year } ; 1978 } }
for the location records of all rows , most of them fuzzily match to united states .
Output: most_eq { all_rows ; location ; united states }
select the rows whose team record does not match to n/a . the number of such rows is 6 .
Output: eq { count { filter_not_eq { all_rows ; team ; n/a } } ; 6 }
select the rows whose method record fuzzily matches to decision . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; method ; decision } } ; 7 }
select the row whose candidates record of all rows is maximum . the district record of this row is georgia 1 .
Output: eq { hop { argmax { all_rows ; candidates } ; district } ; georgia 1 }
select the row whose appearances record of all rows is maximum . the name record of this row is philippe jeannol .
Output: eq { hop { argmax { all_rows ; appearances } ; name } ; philippe jeannol }
the average of the goals record of all rows is 63.78 .
Output: round_eq { avg { all_rows ; goals } ; 63.78 }
select the rows whose team record fuzzily matches to tomcat racing . select the row whose season record of these rows is 3rd minimum . the races record of this row is 12 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; team ; tomcat racing } ; season ; 3 } ; races } ; 12 }
select the rows whose founded record is greater than 1884 . the sum of the enrollment record of these rows is 7,500 .
Output: round_eq { sum { filter_greater { all_rows ; founded ; 1884 } ; enrollment } ; 7,500 }
the average of the score record of all rows is 142 .
Output: round_eq { avg { all_rows ; score } ; 142 }
select the row whose year record of all rows is 3rd minimum . the title of movie record of this row is a flight to yesterday 《 飛往昨天的ci006 》 .
Output: eq { hop { nth_argmin { all_rows ; year ; 3 } ; title of movie } ; a flight to yesterday 《 飛往昨天的ci006 》 }
the average of the crowd record of all rows is 16583.33 .
Output: round_eq { avg { all_rows ; crowd } ; 16583.33 }
select the row whose position record of all rows is 2nd maximum . the venue record of this row is osaka , japan .
Output: eq { hop { nth_argmax { all_rows ; position ; 2 } ; venue } ; osaka , japan }
the average of the international tourist arrivals ( 2012 ) record of all rows is 13.4 million .
Output: round_eq { avg { all_rows ; international tourist arrivals ( 2012 ) } ; 13.4 million }
the average of the haat record of all rows is 261 .
Output: round_eq { avg { all_rows ; haat } ; 261 }
select the row whose total tackles record of all rows is maximum . the team record of this row is baltimore .
Output: eq { hop { argmax { all_rows ; total tackles } ; team } ; baltimore }
select the row whose speed ( knots ) record of all rows is maximum . the warship record of this row is magallanes .
Output: eq { hop { argmax { all_rows ; speed ( knots ) } ; warship } ; magallanes }
for the score records of all rows , most of them are greater than 10 .
Output: most_greater { all_rows ; score ; 10 }
for the power ( kw ) records of all rows , most of them are equal to 5 .
Output: most_eq { all_rows ; power ( kw ) ; 5 }
for the co - drivers records of all rows , most of them fuzzily match to mark blundell .
Output: most_eq { all_rows ; co - drivers ; mark blundell }
select the rows whose engine record fuzzily matches to offenhauser l4 . among these rows , select the rows whose chassis record fuzzily matches to trevis . the number of such rows is 1 .
Output: eq { count { filter_eq { filter_eq { all_rows ; engine ; offenhauser l4 } ; chassis ; trevis } } ; 1 }
select the rows whose competition record fuzzily matches to 2000 summer olympics . take the class record of this row . select the rows whose competition record fuzzily matches to 1996 summer olympics . take the class record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; competition ; 2000 summer olympics } ; class } ; hop { filter_eq { all_rows ; competition ; 1996 summer olympics } ; class } }
the sum of the championships record of all rows is 2 .
Output: round_eq { sum { all_rows ; championships } ; 2 }
select the rows whose engine record fuzzily matches to chevrolet . there is only one such row in the table . the year record of this unqiue row is 1992 .
Output: and { only { filter_eq { all_rows ; engine ; chevrolet } } ; eq { hop { filter_eq { all_rows ; engine ; chevrolet } ; year } ; 1992 } }
select the rows whose wins record is not equal to 0 . the number of such rows is 4 .
Output: eq { count { filter_not_eq { all_rows ; wins ; 0 } } ; 4 }
the average of the pick record of all rows is 20.8 .
Output: round_eq { avg { all_rows ; pick } ; 20.8 }
select the row whose date record of all rows is 2nd minimum . the score record of this row is 3 - 6 , 2 - 6 .
Output: eq { hop { nth_argmin { all_rows ; date ; 2 } ; score } ; 3 - 6 , 2 - 6 }
select the rows whose venue record fuzzily matches to brunswick street oval . there is only one such row in the table . the home team record of this unqiue row is fitzroy . the away team record of this unqiue row is collingwood .
Output: and { only { filter_eq { all_rows ; venue ; brunswick street oval } } ; and { eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; home team } ; fitzroy } ; eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; away team } ; collingwood } } }
select the row whose population record of all rows is minimum . the english name record of this row is zhangping city .
Output: eq { hop { argmin { all_rows ; population } ; english name } ; zhangping city }
select the rows whose school / club team record fuzzily matches to eastern washington . there is only one such row in the table . the player record of this unqiue row is henry bekkering .
Output: and { only { filter_eq { all_rows ; school / club team ; eastern washington } } ; eq { hop { filter_eq { all_rows ; school / club team ; eastern washington } ; player } ; henry bekkering } }
the maximum score record of all rows is 9 - 0 .
Output: eq { max { all_rows ; score } ; 9 - 0 }
select the rows whose surface record fuzzily matches to grass . there is only one such row in the table . the tournament record of this unqiue row is australia f2 , australia .
Output: and { only { filter_eq { all_rows ; surface ; grass } } ; eq { hop { filter_eq { all_rows ; surface ; grass } ; tournament } ; australia f2 , australia } }
select the rows whose studio host record fuzzily matches to craig mustard . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; studio host ; craig mustard } } ; 5 }
select the row whose date record of all rows is maximum . the cover model record of this row is lauren hill .
Output: eq { hop { argmax { all_rows ; date } ; cover model } ; lauren hill }
select the rows whose opponent record fuzzily matches to amsterdam admirals . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; opponent ; amsterdam admirals } } ; 1 }
select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the tournament record of this unqiue row is valencia , spain .
Output: and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; tournament } ; valencia , spain } }
select the rows whose name record fuzzily matches to tyler hansbrough . take the height record of this row . select the rows whose name record fuzzily matches to wes miller . take the height record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; tyler hansbrough } ; height } ; hop { filter_eq { all_rows ; name ; wes miller } ; height } }
for the net capacity records of all rows , most of them are greater than 300 mw .
Output: most_greater { all_rows ; net capacity ; 300 mw }
select the rows whose engine record fuzzily matches to bmw . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; engine ; bmw } } ; 4 }
select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is new york giants .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; new york giants }
select the rows whose no of series record is equal to 3 . select the row whose episodes record of these rows is minimum . the actor record of this row is ellen thomas .
Output: eq { hop { argmin { filter_eq { all_rows ; no of series ; 3 } ; episodes } ; actor } ; ellen thomas }
select the row whose episode record of all rows is 1st minimum . the original airdate ( uk ) record of this row is 26 march 2005 .
Output: eq { hop { nth_argmin { all_rows ; episode ; 1 } ; original airdate ( uk ) } ; 26 march 2005 }
select the rows whose position record fuzzily matches to running back . among these rows , select the rows whose round record is greater than 4 . the number of such rows is 3 .
Output: eq { count { filter_greater { filter_eq { all_rows ; position ; running back } ; round ; 4 } } ; 3 }
for the previous conference records of all rows , most of them fuzzily match to independents .
Output: most_eq { all_rows ; previous conference ; independents }
select the rows whose name record fuzzily matches to patricia sosa ( esa ) . take the total ( kg ) record of this row . select the rows whose name record fuzzily matches to nancy niro ( can ) . take the total ( kg ) record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; patricia sosa ( esa ) } ; total ( kg ) } ; hop { filter_eq { all_rows ; name ; nancy niro ( can ) } ; total ( kg ) } }
the average of the length record of all rows is 5:04 .
Output: round_eq { avg { all_rows ; length } ; 5:04 }
select the rows whose year record fuzzily matches to 1996 . take the length record of this row . select the rows whose year record fuzzily matches to 2000 . take the length record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1996 } ; length } ; hop { filter_eq { all_rows ; year ; 2000 } ; length } }
select the rows whose venue record fuzzily matches to windy hill . take the crowd record of this row . select the rows whose venue record fuzzily matches to princes park . take the crowd record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; venue ; windy hill } ; crowd } ; hop { filter_eq { all_rows ; venue ; princes park } ; crowd } }
select the rows whose venue record fuzzily matches to london , england . there is only one such row in the table . the date record of this unqiue row is 6 february 2008 .
Output: and { only { filter_eq { all_rows ; venue ; london , england } } ; eq { hop { filter_eq { all_rows ; venue ; london , england } ; date } ; 6 february 2008 } }
select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high points record fuzzily matches to ray allen . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; ray allen } } ; 3 }
select the rows whose constellation record fuzzily matches to orion . there is only one such row in the table . the designation hd record of this unqiue row is hd 245409 .
Output: and { only { filter_eq { all_rows ; constellation ; orion } } ; eq { hop { filter_eq { all_rows ; constellation ; orion } ; designation hd } ; hd 245409 } }
select the rows whose incumbent record fuzzily matches to george h tinkham . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to james a gallivan . take the first elected record of this row . the first record is equal to the second record .
Output: eq { hop { filter_eq { all_rows ; incumbent ; george h tinkham } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; james a gallivan } ; first elected } }
select the rows whose name record fuzzily matches to telmatosaurus . take the location record of this row . select the rows whose name record fuzzily matches to brachiosaurus . take the location record of this row . the first record does not match to the second record . the location record of the first row is romania . the location record of the second row is usa .
Output: and { not_eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } } ; and { eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; romania } ; eq { hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } ; usa } } }
select the rows whose score record fuzzily matches to 1-0 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; score ; 1-0 } } ; 3 }
select the rows whose school enrollment ( 200810 ) record is greater than 70 . among these rows , select the rows whose football record fuzzily matches to y . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_greater { all_rows ; school enrollment ( 200810 ) ; 70 } ; football ; y } } ; 4 }
select the rows whose release price ( usd ) record fuzzily matches to oem . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; release price ( usd ) ; oem } } ; 5 }
select the rows whose venue record fuzzily matches to oval . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; venue ; oval } } ; 3 }
for the label records of all rows , all of them fuzzily match to linfair records .
Output: all_eq { all_rows ; label ; linfair records }
select the rows whose top - 10 record is equal to 1 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; top - 10 ; 1 } } ; 2 }
select the rows whose date record fuzzily matches to october 7 , 2011 . take the score record of this row . select the rows whose date record fuzzily matches to june 8 , 2012 . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; october 7 , 2011 } ; score } ; hop { filter_eq { all_rows ; date ; june 8 , 2012 } ; score } }
select the rows whose year record is equal to 1999 . there is only one such row in the table . the championship record of this unqiue row is french open .
Output: and { only { filter_eq { all_rows ; year ; 1999 } } ; eq { hop { filter_eq { all_rows ; year ; 1999 } ; championship } ; french open } }
select the row whose total record of all rows is maximum . the gymnast record of this row is li xiaopeng ( chn ) .
Output: eq { hop { argmax { all_rows ; total } ; gymnast } ; li xiaopeng ( chn ) }
the minimum result record of all rows is 2nd .
Output: eq { min { all_rows ; result } ; 2nd }
select the rows whose season record fuzzily matches to 2010 . take the races record of this row . select the rows whose season record fuzzily matches to 2012 . take the races record of this row . the first record is 6 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; season ; 2010 } ; races } ; hop { filter_eq { all_rows ; season ; 2012 } ; races } } ; 6 }
for the status records of all rows , most of them fuzzily match to tour match .
Output: most_eq { all_rows ; status ; tour match }
select the rows whose opponent in the final record fuzzily matches to marianne witmeyer . there is only one such row in the table . the tournament record of this unqiue row is hong kong .
Output: and { only { filter_eq { all_rows ; opponent in the final ; marianne witmeyer } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; marianne witmeyer } ; tournament } ; hong kong } }
select the rows whose games record is greater than 48 . among these rows , select the rows whose goals record is equal to 27 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater { all_rows ; games ; 48 } ; goals ; 27 } } ; 2 }
the average of the swimsuit record of all rows is 8.255 .
Output: round_eq { avg { all_rows ; swimsuit } ; 8.255 }
select the rows whose high points record fuzzily matches to jermaine o'neal . there is only one such row in the table . the team record of this unqiue row is new orleans .
Output: and { only { filter_eq { all_rows ; high points ; jermaine o'neal } } ; eq { hop { filter_eq { all_rows ; high points ; jermaine o'neal } ; team } ; new orleans } }
for the 5th venue records of all rows , all of them fuzzily match to na .
Output: all_eq { all_rows ; 5th venue ; na }
select the rows whose english ( streamline ) record fuzzily matches to unknown . among these rows , select the rows whose english ( pioneer / geneon ) record fuzzily matches to richard cansino . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; english ( streamline ) ; unknown } ; english ( pioneer / geneon ) ; richard cansino } } ; 2 }
for the us viewers ( million ) records of all rows , most of them are greater than or equal to 1.0 .
Output: most_greater_eq { all_rows ; us viewers ( million ) ; 1.0 }
select the rows whose nominee record fuzzily matches to michael cerveris . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; nominee ; michael cerveris } } ; 2 }
select the rows whose tournament record fuzzily matches to pga championship . take the top - 25 record of this row . select the rows whose tournament record fuzzily matches to masters tournament . take the top - 25 record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; tournament ; pga championship } ; top - 25 } ; hop { filter_eq { all_rows ; tournament ; masters tournament } ; top - 25 } }
select the row whose rounds record of all rows is 1st maximum . the team record of this row is phil parsons racing .
Output: eq { hop { nth_argmax { all_rows ; rounds ; 1 } ; team } ; phil parsons racing }
select the rows whose leading scorer record fuzzily matches to carlos boozer . among these rows , select the rows whose score record fuzzily matches to w . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; leading scorer ; carlos boozer } ; score ; w } } ; 4 }
select the rows whose location record fuzzily matches to tokyo , japan . the average of the record record of these rows is 3.5 .
Output: round_eq { avg { filter_eq { all_rows ; location ; tokyo , japan } ; record } ; 3.5 }
for the top - 10 records of all rows , most of them are greater than 0 .
Output: most_greater { all_rows ; top - 10 ; 0 }
for the date records of all rows , all of them fuzzily match to 1 may 1926 .
Output: all_eq { all_rows ; date ; 1 may 1926 }
select the row whose location attendance record of all rows is 2nd maximum . the date record of this row is february 16 .
Output: eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; date } ; february 16 }
select the rows whose score record fuzzily matches to 3 - 1 . there is only one such row in the table . the date record of this unqiue row is december 14 , 1994 .
Output: and { only { filter_eq { all_rows ; score ; 3 - 1 } } ; eq { hop { filter_eq { all_rows ; score ; 3 - 1 } ; date } ; december 14 , 1994 } }
select the rows whose theme record fuzzily matches to gold rush era . there is only one such row in the table . the casino record of this unqiue row is golden nugget .
Output: and { only { filter_eq { all_rows ; theme ; gold rush era } } ; eq { hop { filter_eq { all_rows ; theme ; gold rush era } ; casino } ; golden nugget } }
select the row whose magnitude record of all rows is 2nd maximum . the name record of this row is 2003 bam earthquake .
Output: eq { hop { nth_argmax { all_rows ; magnitude ; 2 } ; name } ; 2003 bam earthquake }
the sum of the rainfall by depth ( mm / year ) record of all rows is 19537 .
Output: round_eq { sum { all_rows ; rainfall by depth ( mm / year ) } ; 19537 }
select the rows whose date record fuzzily matches to mar . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; date ; mar } } ; 3 }
select the rows whose ceased to be queen record fuzzily matches to divorced . there is only one such row in the table . the father record of this unqiue row is leopold vi , duke of austria .
Output: and { only { filter_eq { all_rows ; ceased to be queen ; divorced } } ; eq { hop { filter_eq { all_rows ; ceased to be queen ; divorced } ; father } ; leopold vi , duke of austria } }
select the rows whose result record fuzzily matches to win . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; result ; win } } ; 4 }
select the rows whose club record fuzzily matches to lokomotiva b . take the number of seasons in prva hnl record of this row . select the rows whose club record fuzzily matches to slaven belupo b . take the number of seasons in prva hnl record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; club ; lokomotiva b } ; number of seasons in prva hnl } ; hop { filter_eq { all_rows ; club ; slaven belupo b } ; number of seasons in prva hnl } }
select the rows whose pick record is equal to 3 . there is only one such row in the table . the player record of this unqiue row is dennis ververgaert .
Output: and { only { filter_eq { all_rows ; pick ; 3 } } ; eq { hop { filter_eq { all_rows ; pick ; 3 } ; player } ; dennis ververgaert } }
select the rows whose assets ( us billion ) record is greater than 100 billion . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; assets ( us billion ) ; 100 billion } } ; 2 }
select the row whose tenure record of all rows is 5th minimum . the school record of this row is coventry .
Output: eq { hop { nth_argmin { all_rows ; tenure ; 5 } ; school } ; coventry }
the average of the diameter ( km ) record of all rows is 512.6 .
Output: round_eq { avg { all_rows ; diameter ( km ) } ; 512.6 }
select the rows whose won record is greater than 10 . among these rows , select the rows whose lost record is equal to 3 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater { all_rows ; won ; 10 } ; lost ; 3 } } ; 2 }
select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record is equal to 137 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; score ; 137 } } ; 2 }
select the rows whose location record fuzzily matches to yankee stadium . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location ; yankee stadium } } ; 3 }
select the rows whose lost record fuzzily matches to 15 . take the lost record of this row . select the rows whose lost record fuzzily matches to 15 . take the lost record of this row . the first record is equal to the second record . the lost record of the first row is 15 . the lost record of the second row is 15 .
Output: and { eq { hop { filter_eq { all_rows ; lost ; 15 } ; lost } ; hop { filter_eq { all_rows ; lost ; 15 } ; lost } } ; and { eq { hop { filter_eq { all_rows ; lost ; 15 } ; lost } ; 15 } ; eq { hop { filter_eq { all_rows ; lost ; 15 } ; lost } ; 15 } } }
select the rows whose title record fuzzily matches to ruler . there is only one such row in the table . the state record of this unqiue row is qin .
Output: and { only { filter_eq { all_rows ; title ; ruler } } ; eq { hop { filter_eq { all_rows ; title ; ruler } ; state } ; qin } }
select the row whose attendance record of all rows is 3rd maximum . the home team record of this row is horsham .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; home team } ; horsham }
select the rows whose name record fuzzily matches to eninho . take the goals record of this row . select the rows whose name record fuzzily matches to ko jeong - woon . take the goals record of this row . the first record is greater than the second record . the goals record of the first row is 51 . the goals record of the second row is 42 .
Output: and { greater { hop { filter_eq { all_rows ; name ; eninho } ; goals } ; hop { filter_eq { all_rows ; name ; ko jeong - woon } ; goals } } ; and { eq { hop { filter_eq { all_rows ; name ; eninho } ; goals } ; 51 } ; eq { hop { filter_eq { all_rows ; name ; ko jeong - woon } ; goals } ; 42 } } }
select the rows whose player record fuzzily matches to gus otto . take the pick record of this row . select the rows whose player record fuzzily matches to otis taylor . take the pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; gus otto } ; pick } ; hop { filter_eq { all_rows ; player ; otis taylor } ; pick } }
select the row whose attendance record of all rows is 1st maximum . the home record of this row is montreal .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; home } ; montreal }
select the row whose from record of all rows is 3rd minimum . the player record of this row is lamar green .
Output: eq { hop { nth_argmin { all_rows ; from ; 3 } ; player } ; lamar green }
for the nationality records of all rows , most of them fuzzily match to estonia .
Output: most_eq { all_rows ; nationality ; estonia }
select the rows whose new adherents per year record is greater than 1000000 . among these rows , select the rows whose growth rate record is greater than 1.70 % . there is only one such row in the table . the religion record of this unqiue row is islam .
Output: and { only { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } } ; eq { hop { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } ; religion } ; islam } }