MMTU-benchmark/MMTU huggingface dataset:
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| vessel                   |
|:-------------------------|
| oocl egypt 032w          |
| oocl kuala lumpur 140s   |
| kota lestari 107s        |
| cosco felixstowe 161s    |
| xin hong kong 043w       |
| xin yan tai 207s         |
| constantino p 325s       |
| cga cgm nevada 0gm29w1ma |
| cosco hong kong 155s     |
| xin zhang zhou 041s      |
| tba 001w                 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| c                            |
|:-----------------------------|
| bhagwangola-ii               |
| murshidabad - ###n###jiaganj |
| nabagram                     |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| code   |
|:-------|
| jpn    |
| fra    |
| kor    |
| hkg    |
| tpe    |
| por    |
| swe    |
| ind    |
| aut    |
| bra    |
| eng    |
| cze    |
| rus    |
| blr    |
| bel    |
| pol    |
| cro    |
| egy    |
| slo    |
| sgp    |
| rou    |
| svk    |
| chi    |
| iri    |
| prk    |
| ita    |
| hun    |
| arg    |
| esp    |
| den    |
| ngr    |
| ukr    |
| gre    |
| par    |
| kaz    |
| ned    |
| tha    |
| usa    |
| aus    |
| tur    |
| ksa    |
| srb    |
| ecu    |
| mex    |
| lux    |
| cub    |
| pur    |
| bul    |
| mas    |
| can    |
| uzb    |
| dom    |
| ltu    |
| alg    |
| ina    |
| sui    |
| vie    |
| nzl    |
| mac    |
| pak    |
| sri    |
| uae    |
| per    |
| sco    |
| mdv    |
| col    |
| cgo    |
| tkm    |
| fin    |
| gua    |
| jor    |
| est    |
| mgl    |
| qat    |
| kgz    |
| ban    |
| oma    |
| lbn    |
| irl    |
| ven    |
| isr    |
| lat    |
| aze    |
| civ    |
| tog    |
| phi    |
| nor    |
| wal    |
| mri    |
| guy    |
| mar    |
| tun    |
| bih    |
| jam    |
| mne    |
| crc    |
| rsa    |
| smr    |
| lao    |
| cyp    |
| cmr    |
| pan    |
| bar    |
| kos    |
| yem    |
| mlt    |
| mda    |
| van    |
| ncl    |
| sey    |
| tto    |
| aru    |
| lba    |
| mkd    |
| jey    |
| dma    |
| lca    |
| isl    |
| sen    |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| release 2 (in points)                         |
|:----------------------------------------------|
| forecast: velocity                            |
| forecast: work remaining at the end of sprint |
| actuals                                       |
| actual: work remaining at start of sprint     |
| actual velocity                               |
| actual work remaining at the end of sprint    |
| team capacity                                 |
| forecast: planned resources                   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|   3 month rolling average 16 |
|-----------------------------:|
|                  -0.0236411  |
|                  -0.0151029  |
|                  -0.0124581  |
|                  -0.0159312  |
|                   0.0019043  |
|                   0.0163495  |
|                   0.0250787  |
|                   0.0224506  |
|                   0.0271235  |
|                   0.0382526  |
|                   0.037178   |
|                   0.0359174  |
|                   0.0293366  |
|                   0.0257039  |
|                   0.0239475  |
|                   0.0160885  |
|                  -0.00390115 |
|                  -0.0117061  |
|                  -0.00865515 |
|                  -0.00137715 |
|                  -0.0029907  |
|                  -0.00767373 |
|                  -0.0016773  |
|                   0.00513405 |
|                   0.00855605 |
|                   0.0120021  |
|                   0.0156666  |
|                   0.0285031  |
|                   0.0363023  |
|                   0.0385301  |
|                   0.038193   |
|                   0.0469662  |
|                   0.0584648  |
|                   0.0576832  |
|                   0.0477124  |
|                   0.0404048  |
|                   0.0405061  |
|                   0.0460199  |
|                   0.0474985  |
|                   0.037221   |
|                   0.0239527  |
|                   0.0128184  |
|                   0.00958015 |
|                   0.00796024 |
|                   0.00870636 |
|                   0.0152964  |
|                   0.0239278  |
|                   0.0314841  |
|                   0.0312878  |
|                   0.0289983  |
|                   0.0178976  |
|                   0.0255586  |
|                   0.045977   |
|                   0.0578122  |
|                   0.0498731  |
|                   0.0386523  |
|                   0.0407642  |
|                   0.0446089  |
|                   0.0380719  |
|                   0.0308868  |
|                   0.0283871  |
|                   0.0270267  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|   % of closings |
|----------------:|
|       0.103448  |
|       0.0574713 |
|       0.045977  |
|       0.0344828 |
|       0.0229885 |
|       0.0114943 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|            p |
|-------------:|
| -8.62316e-05 |
| -0.0100273   |
|  0.00976572  |
| -0.022968    |
|  0.00241705  |
| -0.0173052   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| ecurie           |
|:-----------------|
| ferrari          |
| bmw sauber       |
| renault          |
| toyota           |
| toro rosso       |
| red bull         |
| williams         |
| force india      |
| brawn gp         |
| honda            |
| mclaren mercedes |
| super aguri      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| i             |
|:--------------|
| grand masters |
| master        |
| veteran       |
| senior        |
| u/21          |
| u/16          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| h                                                                    |
|:---------------------------------------------------------------------|
| 17 - upgrade freeway to interstate standards                         |
| 1 - widen existing roadway                                           |
| 11 - access management                                               |
| 16 - modernize roadway                                               |
| 2 - upgrade arterial to freeway/expressway                           |
| 5 - construct roadway on new location                                |
| 6 - widen existing roadway and construct part on new location        |
| 10 - improve intersection                                            |
| 7 - upgrade at-grade intersection to interchange or grade separation |
| 8 - improve interchange                                              |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| subject   |
|:----------|
| acc       |
| ba        |
| bcom      |
| bl        |
| es        |
| fin       |
| mkt       |
| mo        |
| strategy  |
| to        |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| department                           |
|:-------------------------------------|
| autonomous region in muslim mindanao |
| civil service commission             |
| commission on audit                  |
| commission on elections              |
| commission on human rights           |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| description                                     |
|:------------------------------------------------|
| global coal washeries                           |
| global oil refineries                           |
| global natural gas processing plants            |
| global coal extraction                          |
| global oil extraction                           |
| global natural gas extraction                   |
| global biomass pelletization                    |
| global conversion plant (dry biomass to liquid) |
| global conversion plant (dry biomass to gas)    |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| vehicle type.1   |
|:-----------------|
| heavy rail       |
| light rail       |
| over-road coach  |
| streetcar        |
| transit bus      |
| van              |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| school level      |
|:------------------|
| high school level |
| middle level      |
| elementary level  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| 14              |
|:----------------|
| miling          |
| yuna            |
| espada          |
| ega bonnie rock |
| dataine         |
| dart            |
| estoc           |
| emu rock        |
| ega gregory     |
| envoy           |
| impose cl plus  |
| ega wentworth   |
| cosmick         |
| gladius         |
| derrimut        |
| justica cl plus |
| fortune         |
| king rock       |
| grenade cl plus |
| kord cl plus    |
| magenta         |
| westonia        |
| mace            |
| trojan          |
| yitpi           |
| supreme         |
| correll         |
| wmaa11beve6     |
| wmaa12york6     |
| wmaa11yuna6     |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| region           |
|:-----------------|
| arizona          |
| atlanta          |
| colorado         |
| connecticut      |
| florida          |
| greater nyc      |
| hawaii           |
| illinois         |
| indiana          |
| massachusetts    |
| md               |
| michigan         |
| missouri         |
| new jersey       |
| no california    |
| ohio             |
| other            |
| xoutlets         |
| so california    |
| texas            |
| upstate ny       |
| virginia         |
| washington state |


label: xoutlets
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| componentes o subsistemas de desarrollo administrativo   |
|:---------------------------------------------------------|
| 1.2. otros                                               |
| 1.1. indicadores y metas de gobierno                     |
| 5.1. programación y ejecución presupuestal               |
| 3.4. bienestar e incentivos                              |
| 3.3. capacitación                                        |
| 4.2. eficiencia administrativa y cero papel              |
| 4.3. racionalización de trámites                         |
| 4.6. gestión documental                                  |
| 5.2. pac                                                 |
| 2.2. transparencia y acceso a la información pública     |
| 5.4. plan anual de adquisiciones                         |
| 2.1. plan anticorrupción y de atención al ciudadano      |
| 5.3. proyectos de inversión                              |
| 4.1. gestión de la calidad                               |
| 2.5. servicio al ciudadano                               |
| 2.4. rendición de cuentas                                |
| 2.3. participación ciudadana                             |
| 4.5. gestión de tecnologías de información               |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| risk_cat   |
|:-----------|
| medium     |
| very high  |
| high       |
| low        |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| community           |
|:--------------------|
| anoka               |
| apple valley        |
| arden hills         |
| bayport             |
| blaine              |
| bloomington         |
| brooklyn center     |
| brooklyn park       |
| burnsville          |
| chanhassen          |
| chaska              |
| columbia heights    |
| coon rapids         |
| cottage grove       |
| eagan               |
| east bethel         |
| eden prairie        |
| edina               |
| falcon heights      |
| farmington          |
| forest lake         |
| fridley             |
| golden valley       |
| hastings            |
| hopkins             |
| inver grove heights |
| lakeville           |
| lino lakes          |
| little canada       |
| long lake           |
| maple grove         |
| maple plain         |
| maplewood           |
| medina              |
| mendota heights     |
| minneapolis         |
| minnetonka          |
| mounds view         |
| msp intl airport    |
| new brighton        |
| new hope            |
| oakdale             |
| plymouth            |
| prior lake          |
| ramsey              |
| rosemount           |
| roseville           |
| savage              |
| shakopee            |
| shoreview           |
| south st paul       |
| spring lake park    |
| spring park         |
| st anthony          |
| st bonifacius       |
| st louis park       |
| st paul             |
| st paul park        |
| stillwater          |
| vadnais heights     |
| waconia             |
| west st paul        |
| white bear lake     |
| white bear township |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| i                          |
|:---------------------------|
| r                          |
| bd                         |
| nu                         |
| r   - trf                  |
| xor                        |
| c                          |
| nu###n###c                 |
| nu###n###                  |
| c - cred, debt, shar, slev |
| bd###n###c                 |
| nu###n###bd                |
| nu###n### r                |
| nu###n###xor               |
| nu###n###r                 |
| bd/c                       |
| x0r                        |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| Unnamed: 0                         |
|:-----------------------------------|
| heater.liquid.boiler.urban.space   |
| heater.gas.boiler.urban.space      |
| heater.heatpump.urban.space        |
| heater.electricity.urban.space     |
| heater.solar.urban.space           |
| heater.microchp.urban.space        |
| heater.districtheating.urban.space |
| heater.solid.boiler.rural.space    |
| heater.liquid.boiler.rural.space   |
| heater.gas.boiler.rural.space      |
| heater.heatpump.rural.space        |
| heater.electricity.rural.space     |
| heater.solar.rural.space           |
| heater.microchp.rural.space        |
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input     |
|:----------|
| aeiou     |
| aeíouíóúz |
| aeíouxyz  |

Output Table:
| Input     | Output    |
|:----------|:----------|
| aeiou     | aeiou     |
| aeíouíóúz | aeiouiouz |
| aeíouxyz  | aeiouxyz  |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_accent.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
|      Input |
|-----------:|
| 4254423457 |
| 4259876543 |
| 4251239876 |

Output Table:
|      Input | Output         |
|-----------:|:---------------|
| 4254423457 | (425) 442-3457 |
| 4259876543 | (425) 987-6543 |
| 4251239876 | (425) 123-9876 |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_phone.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input                                     |
|:------------------------------------------|
| principle of programming lanaguages       |
| relational data base management system    |
| special interest group management of data |

Output Table:
| Input                                     | Output   |
|:------------------------------------------|:---------|
| principle of programming lanaguages       | popl     |
| relational data base management system    | rdbms    |
| special interest group management of data | sigmod   |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_acronym_ff_fail.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input   |
|:--------|
| ** [[Mala Sinha]]  ''[[Dhool Ka Phool]]'' as Meena Khosla         |
| ** [[Vyjayanthimala]]  ''[[Madhumati]]'' as Madhumati / Madhavi / Radha         |
| * '''1953 [[Meena Kumari]]  ''[[Baiju Bawra (film)|Baiju Bawra]]''''' as '''Gauri'''         |
| ** [[Meena Kumari]]  ''[[Sahara (1958 film)|Sahara]]'' as Leela         |
| * '''1956 [[Nutan]]  ''[[Seema (1955 film)|Seema]]''''' as '''Gauri'''         |
| * '''1955 [[Kamini Kaushal]]  ''[[Biraj Bahu]]''''' as '''Biraj Chakravorty'''         |
| * '''1954 [[Meena Kumari]]  ''[[Parineeta (1953 film)|Parineeta]]''''' as '''Lalita'''         |

Output Table:
| Input   | Output         |
|:--------|:---------------|
| ** [[Mala Sinha]]  ''[[Dhool Ka Phool]]'' as Meena Khosla         | dhool ka phool |
| ** [[Vyjayanthimala]]  ''[[Madhumati]]'' as Madhumati / Madhavi / Radha         | madhumati      |
| * '''1953 [[Meena Kumari]]  ''[[Baiju Bawra (film)|Baiju Bawra]]''''' as '''Gauri'''         | baiju bawra    |
| ** [[Meena Kumari]]  ''[[Sahara (1958 film)|Sahara]]'' as Leela         | sahara         |
| * '''1956 [[Nutan]]  ''[[Seema (1955 film)|Seema]]''''' as '''Gauri'''         | seema          |
| * '''1955 [[Kamini Kaushal]]  ''[[Biraj Bahu]]''''' as '''Biraj Chakravorty'''         | biraj bahu     |
| * '''1954 [[Meena Kumari]]  ''[[Parineeta (1953 film)|Parineeta]]''''' as '''Lalita'''         | parineeta      |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_stringcase2.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
|    Input |
|---------:|
| 19700101 |
| 19111122 |
| 19330212 |

Output Table:
|    Input | Output     |
|---------:|:-----------|
| 19700101 | 01-01-1970 |
| 19111122 | 11-22-1911 |
| 19330212 | 02-12-1933 |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_dob.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input             |
|:------------------|
| 12_2011_xaz.xls   |
| 11_2012_rrrrr.xls |
| 04_2015_rrrrr.xls |

Output Table:
| Input             | Output     |
|:------------------|:-----------|
| 12_2011_xaz.xls   | 12/01/2011 |
| 11_2012_rrrrr.xls | 11/01/2012 |
| 04_2015_rrrrr.xls | 04/01/2015 |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_extractdate.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input                                                         |
|:--------------------------------------------------------------|
| Indy 4 aka Indiana Jones and the Kingdom of the Crystal Skull |
| New X man aka X-Men: Days of Future Past                      |
| New Star Trek aka Star Trek Into Darkness                     |

Output Table:
| Input                                                         | Output                                             |
|:--------------------------------------------------------------|:---------------------------------------------------|
| Indy 4 aka Indiana Jones and the Kingdom of the Crystal Skull | Indiana Jones and the Kingdom of the Crystal Skull |
| New X man aka X-Men: Days of Future Past                      | X-Men: Days of Future Past                         |
| New Star Trek aka Star Trek Into Darkness                     | Star Trek Into Darkness                            |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_aka_ff_fail.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input                                                                                                                          |
|:-------------------------------------------------------------------------------------------------------------------------------|
| | styple = "background #d0e7ff;"|{{Sort|Green Bay Packers 02|[[1967 Green Bay Packers season|Green Bay Packers]]<sup>^</sup>}} |
| | styple = "background #d0e7ff;"|{{Sort|Green Bay Packers 02|[[1911 Green Bay Packers season|Green Bay Packers]]<sup>^</sup>}} |
| | styple = "background #d0e7ff;"|{{Sort|New York Jets 01|[[1968 Green Bay Packers season|New York Jets]]<sup>^</sup>}}         |

Output Table:
| Input                                                                                                                          | Output            |
|:-------------------------------------------------------------------------------------------------------------------------------|:------------------|
| | styple = "background #d0e7ff;"|{{Sort|Green Bay Packers 02|[[1967 Green Bay Packers season|Green Bay Packers]]<sup>^</sup>}} | Green Bay Packers |
| | styple = "background #d0e7ff;"|{{Sort|Green Bay Packers 02|[[1911 Green Bay Packers season|Green Bay Packers]]<sup>^</sup>}} | Green Bay Packers |
| | styple = "background #d0e7ff;"|{{Sort|New York Jets 01|[[1968 Green Bay Packers season|New York Jets]]<sup>^</sup>}}         | New York Jets     |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_duke_ds_2.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv