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:
| fecha límite de presentación de proposiciones / vigencia del anuncio   |
|:-----------------------------------------------------------------------|
| 06/06/2014                                                             |
| 05/06/2014                                                             |
| 03/06/2014                                                             |
| 02/06/2014                                                             |
| 27/05/2014                                                             |
| 04/06/2014                                                             |
| 30/05/2014                                                             |
| 13/06/2014                                                             |
| 10/06/2014                                                             |
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:
| represented nation   |
|:---------------------|
| russia               |
| poland               |
| estonia              |
| finland              |
| canada               |
| france               |
| us                   |
| sweden               |
| czech republic       |
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:
| har svar??                                                                   |
|:-----------------------------------------------------------------------------|
| vil gerne byttes_ men er ikke strengt nødvendig - pga kursus på cesu den dag |
| flyttet fra introugen den 31/8 sidste forelæsning                            |
| obs to forelæsninger samlet i en_ efter aftlae med af og om den 4/10         |
| flyttet fra 4/10 kl. 14.15-1600                                              |
| flyttet fra 13/12 kl 13:15-15                                                |
| flyttet fra samme dag fra 11:15-12:00                                        |
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:
| bc     |
|:-------|
| g 2305 |
| 2312/2 |
| 1013g2 |
| 2z las |
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:
| end date (mm/dd/yy)   |
|:----------------------|
| 07/30/15              |
| 07/23/15              |
| 06/25/15              |
| 08/29/15              |
| 07/08/15              |
| 08/07/15              |
| 08/14/15              |
| 07/03/15              |
| 06/30/15              |
| 07/31/15              |
| 07/10/15              |
| 07/02/15              |
| 08/11/15              |
| 07/28/15              |
| 07/09/15              |
| 06/27/15              |
| 07/24/15              |
| 08/13/15              |
| 08/21/15              |
| 06/26/15              |
| 06/23/15              |
| 06/24/15              |
| 06/19/15              |
| 07/07/15              |
| 06/18/15              |
| 07/17/15              |
| 07/12/15              |
| 08/05/15              |
| 07/01/15              |
| 08/28/15              |
| 08/22/15              |
| 08/31/15              |
| 08/27/15              |
| 08/19/15              |
| 08/23/15              |
| 08/06/15              |
| 07/22/15              |
| 08/12/15              |
| 08/15/15              |
| 06/29/15              |
| 08/20/15              |
| 07/11/15              |
| 08/01/15              |
| 06/12/15              |
| 07/15/15              |
| 08/24/15              |
| 07/16/15              |
| 07/04/15              |
| 07/05/15              |
| 08/17/15              |
| 07/26/15              |
| 08/18/15              |
| 08/02/15              |
| 08/30/15              |
| 07/14/15              |
| 07/20/15              |
| 07/29/15              |
| 06/10/15              |
| 06/16/15              |
| 06/09/15              |
| 07/27/15              |
| 08/04/15              |
| 08/10/15              |
| 06/11/15              |
| 06/28/15              |
| 08/03/15              |
| 08/08/15              |
| 07/06/15              |
| 08/16/15              |
| 07/18/15              |
| 07/13/15              |
| 06/17/15              |
| 08/09/15              |
| 06/02/15              |
| 06/13/15              |
| 07/21/15              |
| 08/26/15              |
| 07/25/15              |
| 06/22/15              |
| 06/04/15              |
| 06/20/15              |
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:
| tuesday                                   |
|:------------------------------------------|
| jeep tour of western island               |
| lunch (12:30)                             |
| drive back to camp                        |
| free time                                 |
| dinner (6:00)                             |
| skype session with astronomy tower (vlba) |
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 2   |
|:----------|
| g3        |
| g14       |
| g2        |
| g4        |
| g5        |
| g6        |
| g7        |
| g8        |
| g9        |
| g10       |
| g11       |
| g12       |
| g13       |
| g15       |
| g16       |
| g17       |
| g18       |
| g19       |
| g20       |
| g21       |
| g22       |
| g23       |
| g24       |
| g25       |
| g26       |
| g27       |
| g28       |
| g29       |
| g30       |
| g31       |
| g32       |
| g33       |
| g34       |
| g35       |
| g36       |
| g37       |
| g38       |
| g39       |
| g40       |
| g41       |
| g42       |
| g43       |
| g44       |
| g45       |
| g46       |
| g47       |
| g48       |
| g49       |
| g50       |
| g51       |
| g52       |
| g53       |
| g54       |
| g55       |
| g56       |
| g57       |
| g58       |
| g59       |
| g60       |
| g61       |
| g62       |
| g63       |
| g64       |
| g65       |
| g66       |
| g67       |
| g68       |
| g69       |
| g70       |
| g71       |
| g72       |
| g73       |
| g74       |
| g75       |
| g76       |
| g77       |
| g78       |
| g79       |
| g80       |
| g81       |
| g82       |
| g83       |
| g84       |
| g85       |
| g86       |
| g87       |
| g88       |
| g89       |
| g90       |
| g91       |
| g92       |
| g93       |
| g94       |
| g95       |
| g96       |
| g97       |
| g98       |
| g99       |
| g100      |
| g101      |
| g102      |
| g103      |
| g104      |
| g105      |
| g106      |
| g107      |
| g108      |
| g109      |
| g110      |
| g111      |
| g112      |
| g113      |
| g114      |
| g115      |
| g116      |
| g117      |
| g118      |
| g119      |
| g120      |
| g121      |
| g122      |
| g123      |
| g124      |
| g125      |
| g126      |
| g127      |
| g128      |
| g129      |
| g130      |
| g131      |
| g132      |
| g133      |
| g134      |
| g135      |
| g136      |
| g137      |
| g138      |
| g139      |
| g140      |
| g141      |
| g142      |
| g143      |
| g144      |
| g145      |
| g146      |
| g147      |
| g148      |
| g149      |
| g150      |
| g151      |
| g152      |
| g153      |
| g154      |
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:
| is main secode                    |
|:----------------------------------|
| n                                 |
| y                                 |
| secode not found in mapping table |
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                                           |
|:--------------------------------------------|
| external loans                              |
| loans from other group companies            |
| loans to other group companies              |
| other amounts due to/(from) group companies |
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:
| d             |
|:--------------|
| (line 3 /52)  |
| (line 3 /260) |
| (line 3 /365) |
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:
| wohnort   |
|:----------|
| münchen   |
| berlin    |
| koblenz   |
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:
| tipo de vehiculo   |
|:-------------------|
| tractocamiones     |
| furgonetas         |
| buses              |
| automóvil          |
| camperos           |
| camionetas         |
| camiones           |
| veh. especiales    |
| chasis bus         |
| hibridos buses     |
| hibridos automóvil |
| eléctricos         |
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:
| l                             |
|:------------------------------|
| high                          |
| good                          |
| good ecological potential     |
| moderate                      |
| poor                          |
| moderate ecological potential |
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:
| calculations               |
|:---------------------------|
| 2021-22 amount             |
| initial difference         |
| total exceptions           |
| difference with exceptions |
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:
| typ                |
|:-------------------|
| sprinter mobil     |
| 2 säulenbühne      |
| klimagerät         |
| 2 stempelbühne     |
| scherenbühne       |
| au gerät           |
| achsmeßgerät       |
| wuchtmaschine      |
| montiermaschine    |
| leistungsprüfstand |
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:
| fw   |
|:-----|
| b4   |
| a4   |
| d4   |
| d5   |
| a5   |
| a6   |
| d6   |
| c4   |
| c5   |
| b5   |
| c1   |
| b6   |
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:
| red                        |
|:---------------------------|
| cutervo                    |
| cajamarca                  |
| chota                      |
| jaen                       |
| cajabamba                  |
| san ignacio                |
| santa cruz                 |
| celendin                   |
| socota                     |
| bambamarca                 |
| no pertenece a ninguna red |
| san miguel                 |
| contumaza                  |
| san pablo                  |
| san marcos                 |
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   |
| menominee 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:
| tipo de inmueble   |
|:-------------------|
| casa habitación    |
| local comercial    |
| predio urbano      |
| predio rustico     |
| predio             |
| industrial         |
| comercial          |
| habitacional       |
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                          |
|:------------------------------------|
| m_notdefined                        |
| m_road                              |
| m_road, m_rail                      |
| m_road, m_rail, m_aviation, m_water |
| m_road, m_water                     |
| m_water                             |
| m_water, m_aviation                 |
| m_water; m_rail                     |
| m_water; m_rail; m_road             |
| m_water; m_rail; m_road; m_aviation |
| m_road; m_aviation                  |
| m_trucks                            |
| m_aviation                          |
| m_road, m_walking                   |
| m_bus                               |
| m_aviation, m_water                 |
| m_notdefined, m_water               |
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:
| lead researcher family name   |
|:------------------------------|
| phillips                      |
| baker                         |
| gray                          |
| muru-lanning                  |
| gott                          |
| osborne                       |
| serlachius                    |
| silander                      |
| roskruge                      |
| furkert                       |
| mackay                        |
| simpson                       |
| noy                           |
| mcarthur                      |
| morpeth                       |
| beasley                       |
| liu                           |
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:
| e-mail                                               |
|:-----------------------------------------------------|
| district.berendrechtzandvlietlillo@stad.antwerpen.be |
| district.borgerhout@stad.antwerpen.be                |
| district.deurne@stad.antwerpen.be                    |
| ekerendistrictssecretaris@stad.antwerpen.be          |
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:
| duration description                   |
|:---------------------------------------|
| 15 years.                              |
| 2 years.                               |
| 10 years                               |
| 5 years.                               |
| 10 years.                              |
| eligible for a waiver after five years |
| five years                             |
| 10 years since sentence completion.    |
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:
| ratingvalue          |
|:---------------------|
| improvement required |
| pass                 |
| pass and eat safe    |
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:
| pais                      |
|:--------------------------|
| perú                      |
| colombia                  |
| extranjero                |
| china                     |
| chile                     |
| india                     |
| alemania                  |
| japon                     |
| mexico                    |
| estados unidos de america |
| panama                    |
| uruguay                   |
| puerto rico               |
| italia                    |
| bolivia                   |
| brasil                    |
| estados unidos            |
| argentina                 |
| pakistan                  |
| republica checa           |
| costa rica                |
| francia                   |
| tailandia                 |
| paraguay                  |
| corea del sur             |
| malasia                   |
| ecuador                   |
| españa                    |
| republica dominicana      |
| indonesia                 |
| canada                    |
| suiza                     |
| bulgaria                  |
| corea del norte           |
| belgica                   |
| australia                 |
| dinamarca                 |
| peru                      |
| taiwan                    |
| venezuela                 |
| vietnam                   |
| suecia                    |
| austria                   |
| holanda                   |
| reino unido               |
| irlanda                   |
| hungria                   |
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:
|         g |
|----------:|
| 0.0125221 |
| 0.0132081 |
| 0.0134221 |
| 0.0135737 |
| 0.0135877 |
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                        |
|:-------------------------|
| adidas fc                |
| pea old boys             |
| bondy fc                 |
| paris crew football as b |
| pereire                  |
| moustache molle fc       |
| estudiente pfc           |
| cosmofoot 75 c           |
| kevin fc                 |
| les dockers              |
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                                                             |
|:--------------------------------------------------------------|
| ebitdar                                                       |
| operating ebitda                                              |
| depreciation and amortisation                                 |
| operating profit (loss) without the effects of one-off events |
| result of one-off events                                      |
| operating profit (loss) (ebit)                                |
| share of net losses of associates                             |
| finance income/(costs)                                        |
| income tax                                                    |
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 |
|-----------:|
| 1.5856e-17 |
| 6.0874e-05 |
| 1.2234e-10 |
| 5.3744e-06 |
| 9.5849e-06 |
| 5.2868e-07 |
| 5.5594e-06 |
| 0.0002066  |
| 1.0585e-09 |
| 6.8555e-12 |
| 1.1309e-10 |
| 2.4235e-15 |
| 1.2566e-15 |
| 0.0095549  |
| 0.0015558  |
| 1.0392e-05 |
| 3.5922e-07 |
| 9.7133e-13 |
| 1.9707e-11 |
| 3.2055e-05 |
| 1.4199e-16 |
| 0.010744   |
| 0.029237   |
| 2.3896e-13 |
| 1.8854e-12 |
| 1.4398e-08 |
| 8.6209e-08 |
| 4.0601e-22 |
| 5.7671e-16 |
| 8.9925e-16 |
| 1.532e-16  |
| 0.002406   |
| 5.5466e-09 |
| 4.8841e-14 |
| 3.9546e-06 |
| 7.1056e-15 |
| 0.00010163 |
| 1.4013e-06 |
| 1.4204e-05 |
| 3.0348e-09 |
| 8.7799e-10 |
| 4.1713e-12 |
| 0.0097664  |
| 1.1052e-18 |
| 7.5632e-08 |
| 4.6437e-09 |
| 9.2781e-14 |
| 0.017559   |
| 2.5877e-16 |
| 7.6612e-06 |
| 1.4086e-12 |
| 1.0044e-17 |
| 1.1725e-13 |
| 5.3655e-07 |
| 6.7014e-10 |
| 4.9997e-07 |
| 1.0489e-09 |
| 0.0012508  |
| 8.939e-05  |
| 5.7627e-07 |
| 3.916e-15  |
| 6.6185e-13 |
| 1.3537e-10 |
| 3.2934e-19 |
| 0.00054376 |
| 5.7697e-10 |
| 4.2732e-05 |
| 0.0013785  |
| 6.8461e-13 |
| 1.1427e-13 |
| 7.2726e-15 |
| 1.8463e-10 |
| 5.7913e-08 |
| 8.4491e-09 |
| 0.00011845 |
| 7.7582e-08 |
| 1.8164e-05 |
| 7.1192e-08 |
| 4.1775e-12 |
| 1.2197e-10 |
| 3.0562e-08 |
| 1.1295e-09 |
| 3.8492e-20 |
| 6.1959e-12 |
| 2.5363e-09 |
| 3.8163e-11 |
| 6.5263e-12 |
| 4.3841e-07 |
| 1.1271e-19 |
| 1.107e-14  |
| 9.4164e-11 |
| 1.7369e-11 |
| 4.1503e-15 |
| 6.1842e-17 |
| 9.1254e-07 |
| 0.00020837 |
| 0.00032911 |
| 1.0509e-07 |
| 4.4338e-07 |
| 2.1913e-07 |
| 3.0272e-05 |
| 1.306e-10  |
| 1.8884e-06 |
| 1.8908e-10 |
| 1.827e-21  |
| 2.8849e-10 |
| 4.9627e-07 |
| 0.0011083  |
| 0.00016029 |
| 0.00066652 |
| 0.017112   |
| 5.0067e-05 |
| 2.4676e-05 |
| 3.2617e-10 |
| 5.6696e-15 |
| 0.0047577  |
| 3.812e-07  |
| 2.7091e-05 |
| 3.231e-10  |
| 0.002249   |
| 2.0682e-05 |
| 2.619e-09  |
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:
| street address                     |
|:-----------------------------------|
| 15 new sudbury st.                 |
| 380 westminster st.                |
| 245 w. houston st.                 |
| 130 south elmwood ave.             |
| 555 willard ave.                   |
| 20 washington place                |
| 5000 wissahickon ave.              |
| 1000 liberty ave.                  |
| 31 hopkins plaza                   |
| 210 franklin rd._ sw               |
| 640 fourth ave.                    |
| 1700 clairmont road                |
| 9500 bay pines blvd.               |
| 251 n. main st.                    |
| 6437 garners ferry road            |
| 110 9th ave._ south                |
| 1250 poydras st. suite 200         |
| 345 perry hill road                |
| 1600 e. woodrow wilson ave.        |
| 1240 east ninth st.                |
| 575 north pennsylvania st.         |
| 321 west main st._ suite 390       |
| 2122 west taylor st.               |
| 477 michigan ave                   |
| 5400 west national ave.            |
| 9700 page avenue                   |
| 210 walnut st.                     |
| 3800 village drive                 |
| 1 federal drive                    |
| 155 van gordon st.                 |
| 500 gold ave._ sw                  |
| 550 foothill drive                 |
| 1301 clay st._ north tower         |
| 11000 wilshire blvd.               |
| 3333 north central ave.            |
| 915 2nd ave.                       |
| 444 w. fort st.                    |
| 100 sw main st._ floor 2           |
| 701 clay avenue                    |
| 2200 fort roots drive_ building 65 |
| 125 south main street              |
| 5460 reno corporate drive          |
| 50 carr 165                        |
| 1501 roxas blvd.                   |
| 6900 almeda road                   |
| 1722 i st._ nw                     |
| 275 chestnut st.                   |
| 4300 goodfellow blvd._ bldg. 104   |
| 8810 rio san diego drive           |
| 477 michigan ave.                  |
| 1722 eye street_ nw                |
| 1 va center                        |
| 163 veterans drive                 |
| 2101 elm st.                       |
| 2501 w. 22nd st.                   |
| 5500 east kellogg                  |
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      |
|:----------------|
| boston          |
| braintree       |
| mansfield       |
| louisville      |
| south dennis    |
| hyannis         |
| vista           |
| atlanta         |
| dennis          |
| brockton        |
| escondido       |
| new bedford     |
| foxborough      |
| manlius         |
| dublin          |
| west dennis     |
| west bloomfield |
| jacksonville    |
| brattleboro     |
| dennis port     |
| chicago         |
| new york        |
| round rock      |
| rockville       |
| orleans         |