MMTU-benchmark/MMTU huggingface dataset:
Task: Error-Detect (source dataset: Relational-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   |
|:--------|
| a       |
| b       |
| c       |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| region    |
|:----------|
| northeast |
| west      |
| south     |
| midwest   |
Task: Error-Detect (source dataset: Relational-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:
| reference                                           |
|:----------------------------------------------------|
| www.redwing.com/aircraft1highpremium.pdf            |
| www.redwing.com/aircraft1highstandard.pdf           |
| www.redwing.com/aircraft2highpremium.pdf            |
| www.redwing.com/aircraft2highstandard.pdf           |
| www.singaporeairlines.com/aircraft3highpremium.pdf  |
| www.singaporeairlines.com/aircraft3highstandard.pdf |
Task: Error-Detect (source dataset: Relational-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:
| custom   |
|:---------|
| #ffffff  |
| #00ff00  |
| #ff0000  |
Task: Error-Detect (source dataset: Relational-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:
| date                    |
|:------------------------|
| 2021-09-05 00:00:00.000 |
| 2021-07-10 00:00:00.000 |
| 2021-08-11 00:00:00.000 |
| 2021-08-18 00:00:00.000 |
| 2021-09-14 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-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:
| day name   |
|:-----------|
| mittwoch   |
| donnerstag |
| freitag    |
| samstag    |
| sonntag    |
| montag     |
| dienstag   |
Task: Error-Detect (source dataset: Relational-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:
| fechacierre             |
|:------------------------|
| 2021-05-18 17:22:53.000 |
| 2021-09-15 21:19:17.000 |
| 2021-10-29 20:28:18.000 |
| 2021-10-11 16:23:46.000 |
| 2021-09-01 13:33:57.000 |
| 2022-01-31 21:40:03.000 |
| 2021-10-26 00:09:29.000 |
| 2021-10-26 00:08:21.000 |
Task: Error-Detect (source dataset: Relational-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:
| productcodenew   |
|:-----------------|
| t79              |
| t18              |
| r79              |
| r19              |
| m38              |
| m18              |
| m68              |
| r89              |
| r64              |
| m47              |
| r93              |
| r68              |
| m82              |
Task: Error-Detect (source dataset: Relational-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:
| fin business unit id   |
|:-----------------------|
| dach, nl, pl           |
| kef uk                 |
| france/belgium         |
| eu distribution        |
| eu e-commerce          |
Task: Error-Detect (source dataset: Relational-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:
| provincia                           |
|:------------------------------------|
| madrid                              |
| barcelona                           |
| a coruña                            |
| torrelodones                        |
| madri                               |
| aravaca                             |
| san sebastian de los reyes - madrid |
| guadalajara                         |
| pinto-madrid                        |
| tres cantos                         |
| alcobendas                          |
| alcorcon                            |
| malaga                              |
| palencia                            |
| valencia                            |
| sevilla                             |
| pinto                               |
| pinto (madrid)                      |
| alcobendas-madrid                   |
| las rozas - madrid                  |
| pozuelo alarcon-madr                |
| cadiz                               |
| getafe-madrid                       |
| majadahonda-madrid                  |
| valdemorillo-madrid                 |
| rivas-vaciamadrid                   |
| collado villalba (ma                |
| fuenlabrada - madrid                |
| alcobendas - madrid                 |
| coslada                             |
| illes balears                       |
| ávila                               |
| torrejon ardoz - mad                |
| alcala de henares                   |
| alicante                            |
| girona                              |
| getafe                              |
| segovia                             |
Task: Error-Detect (source dataset: Relational-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:
| month & year   |
|:---------------|
| apr 2016       |
| may 2016       |
| jun 2016       |
| jul 2016       |
| aug 2016       |
| sep 2016       |
| oct 2016       |
| nov 2016       |
| dec 2016       |
| jan 2017       |
| feb 2017       |
| mar 2017       |
| jan 2018       |
| feb 2018       |
| mar 2018       |
| oct 2017       |
| nov 2017       |
| dec 2017       |
| jul 2017       |
| aug 2017       |
| sep 2017       |
| apr 2017       |
| may 2017       |
| jun 2017       |
| apr 2015       |
| may 2015       |
| jun 2015       |
| jul 2015       |
| aug 2015       |
| sep 2015       |
| oct 2015       |
| nov 2015       |
| dec 2015       |
| jan 2016       |
| feb 2016       |
| mar 2016       |
| jan 2020       |
| feb 2020       |
| mar 2020       |
| oct 2019       |
| nov 2019       |
| dec 2019       |
| apr 2019       |
| may 2019       |
| jun 2019       |
| jul 2019       |
| aug 2019       |
| sep 2019       |
| apr 2018       |
| may 2018       |
| jun 2018       |
| jul 2018       |
| aug 2018       |
| sep 2018       |
| oct 2018       |
| nov 2018       |
| dec 2018       |
| jan 2019       |
| feb 2019       |
| mar 2019       |
Task: Error-Detect (source dataset: Relational-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:
| neighborhood    |
|:----------------|
| soho            |
| central park    |
| midtown         |
| upper west side |
| brooklyn        |
| times square    |
| lower manhattan |
| upper east side |
| lower east side |
| chinatown       |
| east village    |
| west village    |
Task: Error-Detect (source dataset: Relational-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:
| genre        |
|:-------------|
| adventure    |
| misc         |
| action       |
| role-playing |
| sports       |
| simulation   |
| racing       |
| shooter      |
| strategy     |
| fighting     |
| puzzle       |
| platform     |
Task: Error-Detect (source dataset: Relational-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:
| day name   |
|:-----------|
| monday     |
| friday     |
| thursday   |
| wednesday  |
| tuesday    |
Task: Error-Detect (source dataset: Relational-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:
| char description   |
|:-------------------|
| lcc                |
| ccm                |
| clcm               |
| lsc                |
| avmc               |
Task: Error-Detect (source dataset: Relational-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:
| type                  |
|:----------------------|
| boat                  |
| railroad train        |
| railroad mass transit |
Task: Error-Detect (source dataset: Relational-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:
| date                    |
|:------------------------|
| 2020-04-04 00:00:00.000 |
| 2020-05-04 00:00:00.000 |
| 2020-06-04 00:00:00.000 |
| 2020-07-04 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-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:
| productteam   |
|:--------------|
| team e        |
| team a        |
| team c        |
| team d        |
| team b        |
Task: Error-Detect (source dataset: Relational-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:
| grupo               |
|:--------------------|
| brochas             |
| manerales           |
| extenciones         |
| rodillos            |
| repuestos           |
| aplicación          |
| espatulas           |
| plasticos           |
| lijas               |
| masking             |
| cuñas               |
| otros               |
| multitono esmalte   |
| acabados metalicos  |
| base agua           |
| anticorrosivos      |
| reductores          |
| removedores         |
| matizantes          |
| alberca             |
| beralkid            |
| poliuretano         |
| summa               |
| 100acryl            |
| elastomerica        |
| berel               |
| kover               |
| selladores          |
| resanadores         |
| estopa              |
| solventes           |
| aerosoles           |
| tintas              |
| kalos tone          |
| kalos tone satin    |
| berelinte           |
| multitono vinilicas |
| berelex             |
| berelex super satin |
| konberel            |
| maquinaria agricola |
| dry fog             |
| transito            |
| corasa              |
| semigloss           |
Task: Error-Detect (source dataset: Relational-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:
| text                                                                                                                                   |
|:---------------------------------------------------------------------------------------------------------------------------------------|
| a cana é utilizada como matéria-prima em uma usina e se transforma em etanol                                                           |
| através do etanol, uma empresa produz o plástico verde                                                                                 |
| e com o plástico verde, as fábricas produzem vários produtos de plástico que deixam o dia a dia das pessoas mais prático e confortável |
| depois do uso, os produtos plásticos podem ser 100% reciclados                                                                         |
| the use of energy in daily human activity releases co² into the atmosphere                                                             |
| sugarcane absorbs carbon dioxide from the environment through photosynthesis                                                           |
| sugarcane is used as a raw material in a mill and turns into ethanol                                                                   |
| using this ethanol, green plastic is able to be produced                                                                               |
Task: Error-Detect (source dataset: Relational-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:
| name              |
|:------------------|
| rizvi syed        |
| mayank tiwari     |
| ishaan divekar    |
| nikhil chaudhary  |
| kartik jagtap     |
| ayub khan         |
| nabeela shah      |
| ayushi sharma     |
| pawan pandey      |
| ashutosh panddey  |
| abhinav singh     |
| sachin santhosh   |
| param shah        |
| sanat bhatt       |
| aalok bhatawdekar |
Task: Error-Detect (source dataset: Relational-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:
| movie_country   |
|:----------------|
| russia          |
| italy           |
| spain           |
| france          |
| china           |
| lebanon         |
Task: Error-Detect (source dataset: Relational-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:
| city                |
|:--------------------|
| mankato             |
| st peter            |
| new ulm             |
| saint paul          |
| inver grove heights |
| winona              |
| faribault           |
| shakopee            |
| lakeville           |
| austin              |
| owatonna            |
| farimont            |
| lake crystal        |
| mapleton            |
| savage              |
| maple grove         |
| sauk center         |
| richfield           |
| moorhead            |
| maplewood           |
| oklahoma city       |
| richmond            |
| juneau              |
| carson city         |
| boise               |
| pierre              |
| little rock         |
| jackson             |
| dover               |
| washington          |
| augusta             |
| columbia            |
| trenton             |
| annapolis           |
| albany              |
| montpelier          |
| concord             |
| sacramento          |
| salem               |
| helena              |
| springfield         |
| atlanta             |
| madison             |
| des moines          |
| chicago             |
| frankfort           |
| columbus            |
| boston              |
| denver              |
| honolulu            |
| salt lake city      |
| nashville           |
| ames                |
| topeka              |
| santa fe            |
| bismarck            |
| new york            |
| montgomery          |
| lansing             |
| harrisburg          |
| indianapolis        |
| phoenix             |
| hartford            |
| olympia             |
| lincoln             |
| providence          |
| raleigh             |
| cheyenne            |
| tallahassee         |
| jefferson city      |


label: farimont
Task: Error-Detect (source dataset: Relational-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:
| closeworkstation   |
|:-------------------|
| x675               |
| r932               |
| r931               |
| r934               |
| r933               |
| r937               |
| r935               |
| r938               |
| r936               |
| r939               |
| x670               |
| x680               |
| x677               |
| b040               |
| b924               |
| v010               |
| b008               |
| r944               |
| b020               |
| b010               |
| x695               |
| b030               |
| x892               |
| x895               |
| v020               |
| x595               |
| x650               |
| x140               |
| x045               |
| x100               |
| x495               |
| x065               |
| x390               |
| x608               |
| v030               |
| x180               |
| x120               |
| r942               |
| x980               |
| v040               |
| x795               |
| x990               |
| x450               |
| x040               |
| x250               |
| x500               |
| x470               |
| r150               |
| c100               |
| c315               |
| r320               |
| r941               |
| c200               |
| c090               |
| b219               |
| r945               |
| r940               |
| b979               |
| r270               |
| r271               |
| v280               |
| b149               |
| b260               |
| v290               |
| b007               |
| b100               |
| b694               |
| a180               |
| b335               |
| b697               |
| b711               |
| a181               |
| b698               |
| b280               |
| x685               |
| v260               |
| r943               |
| a402               |
| b337               |
| a699               |
| x395               |
| x440               |
Task: Error-Detect (source dataset: Relational-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:
| month     |
|:----------|
| july      |
| august    |
| september |
| june      |
| october   |
| april     |
| february  |
| march     |
| may       |
| november  |
| december  |
| january   |
Task: Error-Detect (source dataset: Relational-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:
| enddate                 |
|:------------------------|
| 2021-12-09 00:00:00.000 |
| 2021-02-24 00:00:00.000 |
| 9999-12-31 00:00:00.000 |
| 2021-02-25 00:00:00.000 |


label: 9999-12-31 00:00:00.000
Task: Error-Detect (source dataset: Relational-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:
| pca reference (unicef partners only)             |
|:-------------------------------------------------|
| caofiser/pca/2017/52 (erbil fo)                  |
| hrf/ pca/2016/104 (erbil fo)                     |
| rnvdo/pca/2017/51 (erbil fo)                     |
| ncson/pca/2017/65 (erbil fo)                     |
| ncson/pca/2017/17 (erbil fo)                     |
| acf-fr pca/2016/95 (erbil fo)                    |
| rnvdo/pca/2017/38 (erbil fo)                     |
| qrc_pca/2017/57 (erbil fo)                       |
| qrc pca/2016/99 (erbil fo)                       |
| erbil-fo, ejcc (erbil joint crisis coordination) |
| rirp pca/2016/73 (baghdad fo)                    |
| qrc_pca/2017/114 (erbil fo)                      |
| bifa pca/2016/107 (baghdad fo)                   |
| rirp pca/2016/73 (ico)                           |
| bcf_pca/2017/20 (duhok fo)                       |
| jf pca/2017/22 (baghdad fo)                      |
| qandil_pca/2017/24 (erbil fo)                    |
| reach pca/2017/70 (erbil fo)                     |
| al-ansar charity foundation pca 2016/23 (ico)    |
| rnvdo/pca/2017/03 (erbil fo)                     |
| drc_pca/2017/89 (erbil fo)                       |
| oxfam gb pca/2016/74 (erbil fo)                  |
| reach pca 2016/76 (erbil fo)                     |
| uims pca 2016/67 (baghdad fo)                    |
| ssord pca 2016/66 (baghdad fo)                   |
| al eslah pca/2016/ ( baghdad fo)                 |
| oxfam_pca/2017/61 (erbil fo)                     |
Task: Error-Detect (source dataset: Relational-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:
| continent     |
|:--------------|
| africa        |
| asia          |
| europe        |
| north america |
| oceania       |
| south america |
Task: Error-Detect (source dataset: Relational-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:
| saleschannel             |
|:-------------------------|
| национальная сеть        |
| дистрибьютор             |
| топ-локальная сеть       |
| собственные организации  |
| оптовый                  |
| остальные локальные сети |
| сотрудники               |
| неорганизованная розница |
| не определено            |
Task: Error-Detect (source dataset: Relational-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:
| reserved_room_type   |
|:---------------------|
| a                    |
| e                    |
| d                    |
| c                    |
| g                    |
| f                    |
| b                    |
| h                    |
| l                    |
Task: Error-Detect (source dataset: Relational-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:
| productline   |
|:--------------|
| s             |
| m             |
| r             |
| t             |
Task: Error-Detect (source dataset: Relational-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:
| repr_shopcode   |
|:----------------|
| xh0247          |
| xh0239          |
| xh0222          |
| xh0260          |
| xh271           |
| xh0243          |
| xh269           |
| xh0252          |
| xh0255          |
| xh0201          |
| xh0202          |
| xh0223          |
| xh0257          |
| xh0249          |
| xh0229          |
| xh0254          |
| xh0200          |
| xh0248          |
| xh0231          |
| xh0214          |
| xh0246          |
| xh264           |
| xh0226          |
| xh0266          |
| xh0259          |
| xh268           |
| xh0258          |
| xh0213          |
| xh0216          |
| xh0253          |
| xh0265          |
Task: Error-Detect (source dataset: Relational-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:
| category   |
|:-----------|
| junior     |
| men        |
| mix        |
| women      |
| senior     |
| children   |
Task: Error-Detect (source dataset: Relational-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:
| team        |
|:------------|
| own         |
| distributor |
| fsi         |
| public      |
| corporate   |
Task: Error-Detect (source dataset: Relational-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:
| observacoes                                                                                                                                                                                                                                         |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| prazo condicionado à divulgação das alterações.                                                                                                                                                                                                     |
| as solicitações referentes a lâmpadas queimadas e manutenções diversas podem ser solicitadas também através da ouvidoria pelo telefone 156, opção 5 - ilumina mogi. as solicitações feitas on line ou pelo 156 são isentas de taxas administrativas |
| detalhar o local do problema, de preferência, com um ponto de referência.                                                                                                                                                                           |