germany
the revenue of metro ag is lower than the revenue of daimler ag .
{'row_1': '7', 'row_2': '3', 'col': '4', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'metro ag'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to metro ag .', 'tostr': 'filter_eq { all_rows ; name ; metro ag }'}, 'revenue ( mil )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; metro ag } ; revenue ( mil ) }', 'tointer': 'select the rows whose name record fuzzily matches to metro ag . take the revenue ( mil ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'daimler ag'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to daimler ag .', 'tostr': 'filter_eq { all_rows ; name ; daimler ag }'}, 'revenue ( mil )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; daimler ag } ; revenue ( mil ) }', 'tointer': 'select the rows whose name record fuzzily matches to daimler ag . take the revenue ( mil ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; name ; metro ag } ; revenue ( mil ) } ; hop { filter_eq { all_rows ; name ; daimler ag } ; revenue ( mil ) } } = true', 'tointer': 'select the rows whose name record fuzzily matches to metro ag . take the revenue ( mil ) record of this row . select the rows whose name record fuzzily matches to daimler ag . take the revenue ( mil ) record of this row . the first record is less than the second record .'}
less { hop { filter_eq { all_rows ; name ; metro ag } ; revenue ( mil ) } ; hop { filter_eq { all_rows ; name ; daimler ag } ; revenue ( mil ) } } = true
select the rows whose name record fuzzily matches to metro ag . take the revenue ( mil ) record of this row . select the rows whose name record fuzzily matches to daimler ag . take the revenue ( mil ) record of this row . the first record is less than the second record .
{'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'name_7': 'name', 'metro ag_8': 'metro ag', 'revenue (mil)_9': 'revenue ( mil )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'daimler ag_12': 'daimler ag', 'revenue (mil)_13': 'revenue ( mil )'}
['rank', 'name', 'headquarters', 'revenue ( mil )', 'profit ( mil )', 'employees ( world )']
[['0 1', 'volkswagen ag', 'wolfsburg', '159.000', '15.800', '502 ,000'], ['0 2', 'eon se', 'düsseldorf', '113.000', '1.900', '79 ,000'], ['0 3', 'daimler ag', 'stuttgart', '107.000', '6.000', '271 ,000'], ['0 4', 'siemens ag', 'berlin , münchen', '74.000', '6.300', '360 ,000'], ['0 5', 'basf se', 'ludwigshafen am rhein', '73.000', '6.600', '111 ,000'], ['0 6', 'bmw ag', 'münchen', '69.000', '4.900', '100 ,000'], ['0 7', 'metro ag', 'düsseldorf', '67.000', '740', '288 ,000'], ['0 8', 'schwarz - gruppe ( lidl / kaufland )', 'neckarsulm', '63.000', 'n / a', '315 ,000'], ['0 9', 'deutsche telekom ag', 'bonn', '59.000', '670', '235 ,000'], ['0 10', 'deutsche post ag', 'bonn', '53.000', '1.300', '471 ,000'], ['-', 'allianz se', 'münchen', '104.000', '2.800', '141 ,000'], ['-', 'deutsche bank ag', 'frankfurt am main', '2.160.000', '4.300', '101 ,000']]