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:
| tablename      |
|:---------------|
| schoolsbudget  |
| schoolsbudget2 |
| latotals       |
| latotals2      |
| reconciliation |
| totalline      |
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:
| color      |
|:-----------|
| 53_ 102_ 0 |
| 7_ 125_ 0  |
| 13_ 121_ 0 |
| 20_ 118_ 0 |
| 33_ 112_ 0 |
| 46_ 105_ 0 |
| 26_ 115_ 0 |
| 59_ 98_ 0  |
| 66_ 95_ 0  |
| 39_ 108_ 0 |
| 125_ 66_ 0 |
| 79_ 89_ 0  |
| 92_ 82_ 0  |
| 176_ 39_ 0 |
| 150_ 53_ 0 |
| 118_ 69_ 0 |
| 170_ 43_ 0 |
| 143_ 56_ 0 |
| 157_ 49_ 0 |
| 189_ 33_ 0 |
| 131_ 62_ 0 |
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:
| hsg   |
|:------|
| b     |
| c     |
| a     |
| c/d   |
| b/d   |
| d     |
| ---   |
| a/d   |
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:
| valid values:                                |
|:---------------------------------------------|
| above co-pay under co-pay                    |
| abs statistical area level 3 grouping code.  |
| please see pbs item map for more information |
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:
| consequence category                                                                                                        |
|:----------------------------------------------------------------------------------------------------------------------------|
| employment                                                                                                                  |
| employment; business license and other property rights; political and civic participation                                   |
| employment; registration, notification, and residency restrictions                                                          |
| employment; occupational and professional license and certification                                                         |
| employment; occupational and professional license and certification; registration, notification, and residency restrictions |
| employment; political and civic participation                                                                               |
| employment; occupational and professional license and certification; business license and other property rights             |
| employment; business license and other property rights                                                                      |
| occupational and professional license and certification; business license and other property rights                         |
| employment; business license and other property rights; housing                                                             |
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:
| top word pairs in tweet in g4   |
|:--------------------------------|
| visitneworleans_nolachristmas   |
| fest_kicked                     |
| kicked_13                       |
| 13_days                         |
| days_magical                    |
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:
| quarter   |
|:----------|
| fy 09-q3  |
| fy 09-q4  |
| fy 10-q1  |
| fy 10-q2  |
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:
| mov   |
|:------|
| x     |
| r     |
| e     |
| w     |
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:
| laitos      |
|:------------|
| veritas     |
| alandia     |
| etera       |
| ilmarinen   |
| tapiola     |
| varma       |
| fennia      |
| yhteensä    |
| lähitapiola |
| elo         |
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:
| language   |
|:-----------|
| ja         |
| en         |
| nl         |
| fi         |
| it         |
| und        |
| de         |
| fr         |
| es         |
| ro         |
| et         |
| ht         |
| pl         |
| cy         |
| ru         |
| pt         |
| ar         |
| cs         |
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                                                 |
|:-----------------------------------------------------------|
| ne 14th st & 39th ave behind k-mart                        |
| dra between 1721 & 1716 ne 46th rd in autumn ridge         |
| e. hwy 40 & 49th ct rd - in front of arby`s mow the bottom |
| ne 17th pl & 40th ave - azteca edge sidewalk/trail         |
| 2913 ne 9th st,  behind aldi, sr 40 east                   |
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:
| période    |
|:-----------|
| a          |
| p          |
| h          |
| ecole      |
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:
| event          |
|:---------------|
| view           |
| update         |
| events deleted |
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:
| postcode   |
|:-----------|
| bd7 7ty    |
| nk5 2ah    |
| nk4 5pq    |
| bd2 9lx    |
| nk5 2as    |
| bd5 5af    |
| wv5 2jr    |
| wv6 2ab    |
| wv5 1ab    |
| nk5 4zg    |
| nk4 6fd    |
| bd5 8fs    |
| wv3 8nt    |
| bd4 5af    |
| nk5 4pt    |
| nk5 9pr    |
| nk4 7fg    |
| bd7 8rd    |
| nk4 8hu    |
| bd7 2as    |
| nk5 1pg    |
| bd7 5tg    |
| bd2 9gb    |
| nk5 9lo    |
| wv5 9hs    |
| bd2 9vb    |
| wv5 2yp    |
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            |
|:----------------------|
| classification        |
| total, mapo, gov, dev |
| total, imme, gov, dev |
| total, imme, env, dev |
| total, imme, env      |
| total, imme, soc, dev |
| total, imme, soc      |
| total, mapo, soc      |
| total, mapo, env      |
| dev                   |
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:
| sequence   |
|:-----------|
| aaa##      |
| aaaa#      |
| aaaaa      |
| bb###      |
| bbb##      |
| bbbb#      |
| bbbbb      |
| cc###      |
| ccc##      |
| cccc#      |
| ccccc      |
| ddd##      |
| dddd#      |
| ddddd      |
| eee##      |
| eeee#      |
| eeeee      |
| fff##      |
| ffff#      |
| fffff      |
| jjj##      |
| jjjj#      |
| jjjjj      |
| kkk##      |
| kkkk#      |
| kkkkk      |
| nn###      |
| nnn##      |
| nnnn#      |
| nnnnn      |
| qqq##      |
| qqqq#      |
| qqqqq      |
| ttt##      |
| tttt#      |
| ttttt      |
| gg###      |
| ggg##      |
| gggg#      |
| ggggg      |
| ss###      |
| sss##      |
| ssss#      |
| sssss      |
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:
| top words in tweet in entire graph       |
|:-----------------------------------------|
| words in sentiment list#3: angry/violent |
| non-categorized words                    |
| total words                              |
| rock                                     |
| hard                                     |
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:
| branch     |
|:-----------|
| old-age    |
| survivors  |
| incapacity |
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:
| site_name_alias                        |
|:---------------------------------------|
| thangkhali / burmapara / tasnimarkhola |
| hakimpara                              |
| jamtoli                                |
| bagghona/potibonia                     |
| chakmarkul/kerontuli                   |
| unchiprang                             |
| shamlapur                              |
| leda                                   |
| alikhali                               |
| nayapara                               |
| jadimura                               |
| kutupalong rc                          |
| nayapara rc                            |
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:
| b      |
|:-------|
| violet |
| arya   |
| liam   |
| mike   |
| neal   |
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          |
|:--------------------|
| water boiler        |
| blenders            |
| ovens               |
| panini grill        |
| furniture           |
| dishwasher          |
| ice machine         |
| chillers/freezers   |
| aircon & extraction |
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:
| choose category                             |
|:--------------------------------------------|
| sub station: design and build (turn-key)    |
| overhead line: procurement and construction |
| services: consultants                       |
| overhead line: forest clearing              |
| sub station: transformers_ reactors         |
| services: administrative procurements       |
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:
| volunteer site                                                |
|:--------------------------------------------------------------|
| jesuit volunteer corps                                        |
| cristo rey                                                    |
| jesuit college preparatory school of dallas                   |
| city year                                                     |
| match charter high school                                     |
| mercyworks volunteer program at mercy home for boys and girls |
| peace corps                                                   |
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:
| odsek   |
|:--------|
| must    |
| mfir    |
| infor   |
| mtur    |
| pumps   |
| jmen    |
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:
| county:    |
|:-----------|
| cook       |
| dupage     |
| lake       |
| kane       |
| livingston |
| madison    |
| peoria     |
| mchenry    |
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:
| clube8          |
|:----------------|
| aabb/rizzone    |
| nipo            |
| fitpong         |
| asmett          |
| bye             |
| ass m. nogueira |
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:
| group 1   |
|:----------|
| g1        |
| g2        |
| g3        |
| g4        |
| g5        |
| g6        |
| g7        |
| g8        |
| g9        |
| g10       |
| g11       |
| g12       |
| g13       |
| g14       |
| g15       |
| g16       |
| g17       |
| g18       |
| g19       |
| g20       |
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:
| reason interest not paid                                    |
|:------------------------------------------------------------|
| capital appropriation not subject to prompt contracting law |
| administrative funds unavailable for payment                |
| prompt contracting calculation difficulties                 |
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:
| provider spoken - mandarin    |
|:------------------------------|
| <11                           |
| 3.0000000000000001e-3         |
| 0.02                          |
| 5.0000000000000001e-3         |
| 2e-3                          |
| 1e-3                          |
| 2.9000000000000001e-2         |
| 3.3000000000000002e-2         |
| 6.0000000000000001e-3         |
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:
| county             |
|:-------------------|
| adams county       |
| ashland county     |
| barron county      |
| bayfield county    |
| brown county       |
| buffalo county     |
| burnett county     |
| calumet county     |
| chippewa county    |
| clark county       |
| columbia county    |
| crawford county    |
| dane county        |
| dodge county       |
| door county        |
| douglas county     |
| dunn county        |
| eau claire county  |
| florence county    |
| fond du lac county |
| forest county      |
| grant county       |
| green county       |
| green lake county  |
| iowa county        |
| iron county        |
| jackson county     |
| jefferson county   |
| juneau county      |
| kenosha county     |
| kewaunee county    |
| la crosse county   |
| lafayette county   |
| langlade county    |
| lincoln county     |
| manitowoc county   |
| marathon county    |
| marinette county   |
| marquette county   |
| milwaukee county   |
| monroe county      |
| oconto county      |
| oneida county      |
| outagamie county   |
| ozaukee county     |
| pepin county       |
| pierce county      |
| polk county        |
| portage county     |
| price county       |
| racine county      |
| richland county    |
| rock county        |
| rusk county        |
| st. croix county   |
| sauk county        |
| sawyer county      |
| shawano county     |
| sheboygan county   |
| taylor county      |
| trempealeau county |
| vernon county      |
| vilas county       |
| walworth county    |
| washburn county    |
| washington county  |
| waukesha county    |
| waupaca county     |
| waushara county    |
| winnebago county   |
| wood county        |
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:
| q                   |
|:--------------------|
| martin hockey       |
| gavin denning       |
| mick murphy         |
| paul cohu           |
| russ wakeford       |
| wayne rihoy         |
| stuart crittell     |
| antony makepeace    |
| carl roussel        |
| paul regan          |
| andrew marley       |
| john carre          |
| james walker        |
| chris le vallee     |
| matt strachan       |
| pat le gallez       |
| charlie le poidevin |
| richard henderson   |
| ricky dodd          |
| rob hardwick        |
| ben le noury        |
| barry martel        |
| andrew wilkinson    |
| nick donaldson      |
| darryl young        |
| phil finigan        |
| kate le gallez      |
| mark loveridge      |
| kevin le moigne     |
| lee roome           |
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:
| b          |
|:-----------|
| mark       |
| ryan       |
| donna      |
| colette    |
| michael    |
| dar        |
| kevin      |
| jennifer   |
| jessica    |
| bradford   |
| john       |
| stephanie  |
| jeff       |
| nancy      |
| sebastian  |
| jhoan      |
| michelle   |
| don        |
| ernie      |
| roberleigh |
| melissa    |
| jasmine    |
| rainbow    |
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:
| a     |
|:------|
| n.s.​  |
| n.b.​​  |
| que.​  |
| ont.​  |
| man.​  |
| sask.​ |
| alta.​​ |
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:
| استان              |
|:-------------------|
| آذربايجان غربي     |
| اردبيل             |
| اصفهان             |
| البرز              |
| ايلام              |
| بوشهر              |
| تهران              |
| چهارمحال و بختياري |
| خراسان جنوبي       |
| خراسان رضوي        |
| خراسان شمالي       |
| خوزستان            |
| زنجان              |
| سمنان              |
| سيستان وبلوچستان   |
| فارس               |
| قزوين              |
| قم                 |
| كردستان            |
| كرمان              |
| كرمانشاه           |
| كهگيلويه وبويراحمد |
| گلستان             |
| گيلان              |
| لرستان             |
| مازندران           |
| مركزي              |
| هرمزگان            |
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:
| unit    |
|:--------|
| fl. oz. |
| ounce   |
| each    |
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:
| job title              |
|:-----------------------|
| superintendent         |
| interim superintendent |
| principal              |
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:
| beta lactogobulin   |
|:--------------------|
| ab                  |
| aa                  |
| bb                  |
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:
| sau type code   |
|:----------------|
| c               |
| m               |
| o               |
| v               |
| h               |
| t               |
| s               |
| a               |
| e               |
| r               |
| q               |
| n               |
| p               |
| l               |
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                                  |
|:-----------------------------------|
| outside services - employed - a&g  |
| liability & other insurance        |
| property insurance                 |
| injuries & damages - a&g           |
| employee pensions & benefits - a&g |
| general advertising                |
| misc. general expenses - a&g       |
| rents - a&g                        |