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:
| record                  |
|:------------------------|
| 2019-07-18 00:00:00.000 |
| 2019-04-18 00:00:00.000 |
| 2019-02-07 00:00:00.000 |
| 2018-10-18 00:00:00.000 |
| 2018-07-19 00:00:00.000 |
| 2018-04-19 00:00:00.000 |
| 2018-02-08 00:00:00.000 |
| 2017-10-19 00:00:00.000 |
| 2017-07-20 00:00:00.000 |
| 2017-04-20 00:00:00.000 |
| 2017-02-02 00:00:00.000 |
| 2016-10-20 00:00:00.000 |
| 2016-07-21 00:00:00.000 |
| 2016-04-21 00:00:00.000 |
| 2016-02-04 00:00:00.000 |
| 2015-10-22 00:00:00.000 |
| 2015-07-16 00:00:00.000 |
| 2015-04-16 00:00:00.000 |
| 2015-02-05 00:00:00.000 |
| 2014-10-16 00:00:00.000 |
| 2014-07-17 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:
| id_manco           |
|:-------------------|
| 99 - varios        |
| 01 - el progreso   |
| 02 - chimaltenango |
| 03 - escuintla     |
| 04 - escuintla     |
| 05 - solola        |
| 06 - solola        |
| 07 - solola        |
| 08 - totonicapan   |
| 09 - totonicapan   |
| 03 - suchitepequez |
| 07 - retalhuleu    |
| 10 - huehuetenango |
| 11 - huehuetenango |
| 12 - huehuetenango |
| 13 - quiche        |
| 14 - quiche        |
| 02 - quiche        |
| 15 - quiche        |
| 11 - quiche        |
| 16 - baja verapaz  |
| 17 - alta verapaz  |
| 18 - alta verapaz  |
| 19 - peten         |
| 20 - peten         |
| 17 - izabal        |
| 21 - zacapa        |
| 22 - zacapa        |
| 21 - chiquimula    |
| 23 - chiquimula    |
| 24 - chiquimula    |
| 22 - chiquimula    |
| 23 - jalapa        |
| 25 - jutiapa       |
| 26 - jutiapa       |
| 27 - jutiapa       |
| 28 - jutiapa       |
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:
| location             |
|:---------------------|
| building 10          |
| building 1           |
| building tc22        |
| building tc23        |
| dta                  |
| sez                  |
| reliance greens      |
| administration block |
| area1                |
| area2                |
| area3                |
| area4                |
| area5                |
| area6                |
| area7                |
| area8                |
| area9                |
| area10               |
| area11               |
| area12               |
| area13               |
| area14               |
| area15               |
| area16               |
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_of_week   |
|:--------------|
| tuesday       |
| wednesday     |
| thursday      |
| friday        |
| saturday      |
| sunday        |
| monday        |
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:
| assign date   |
|:--------------|
| 2020/12/2     |
| 2020/12/3     |
| 2016/1/19     |
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:
| ship date               |
|:------------------------|
| 2016-10-08 00:00:00.000 |
| 2017-01-18 00:00:00.000 |
| 2017-10-09 00:00:00.000 |
| 2014-12-21 00:00:00.000 |
| 2015-03-22 00:00:00.000 |
| 2016-05-27 00:00:00.000 |
| 2016-04-20 00:00:00.000 |
| 2014-09-12 00:00:00.000 |
| 2016-10-09 00:00:00.000 |
| 2017-11-04 00:00:00.000 |
| 2017-03-25 00:00:00.000 |
| 2017-10-24 00:00:00.000 |
| 2014-07-27 00:00:00.000 |
| 2014-03-23 00:00:00.000 |
| 2016-02-04 00:00:00.000 |
| 2014-09-28 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:
| modifiedon              |
|:------------------------|
| 2020-07-29 18:06:05.000 |
| 2020-07-31 01:33:59.000 |
| 2020-08-04 02:40:36.000 |
| 2020-08-04 02:40:38.000 |
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:
| month-year   |
|:-------------|
| jul-2019     |
| aug-2019     |
| sep-2019     |
| oct-2019     |
| nov-2019     |
| dec-2019     |
| jan-2020     |
| feb-2020     |
| mar-2020     |
| apr-2020     |
| may-2020     |
| jun-2020     |
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:
| earliest                |
|:------------------------|
| 2019-03-12 00:00:00.000 |
| 2019-03-13 00:00:00.000 |
| 2019-03-03 00:00:00.000 |
| 2020-03-11 00:00:00.000 |
| 2020-03-01 00:00:00.000 |
| 2020-03-13 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:
| nome               |
|:-------------------|
| mrs chloe kirby    |
| mrs louise clark   |
| mr harrison bowen  |
| mrs anna sharp     |
| mrs daisy anderson |
| ms zoe peacock     |
| mrs libby williams |
| ms katie mmtcalfe  |
| mr luca vaughan    |
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:
| wait_category     |
|:------------------|
| buffer io         |
| cpu - parallelism |
| latch             |
| lock              |
| user wait         |
| tran log io       |
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:
| yearquarter   |
|:--------------|
| 2020 q3       |
| 2020 q1       |
| 2020 q2       |
| 2020 q4       |
| 2021 q1       |
| 2021 q4       |
| 2021 q2       |
| 2021 q3       |
| 2022 q3       |
| 2022 q2       |
| 2022 q4       |
| 2022 q1       |
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:
| fname   |
|:--------|
| ab      |
| preksha |
| smita   |
| carry   |
| shrut   |
| paresh  |
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:
| q4   |
|:-----|
| rr   |
| kkr  |
| mi   |
| srh  |
| rcb  |
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:
| column1.value.meter_type                   |
|:-------------------------------------------|
| lease                                      |
| agreement                                  |
| lease to landowner                         |
| susquehanna                                |
| bowmann pennview consumer                  |
| jeremy & melissa smith own gas and surface |
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:
| fiscalyear   |
|:-------------|
| fy16         |
| fy17         |
| fy18         |
| fy19         |
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:
| tipo de venta   |
|:----------------|
| venta regular   |
| portabilidad    |
| renovacion      |
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:
| crp       |
|:----------|
| ccafs     |
| livestock |
| a4nh      |
| bigdata   |
| rice      |
| pim       |
| rtb       |
| fta       |
| wle       |
| gldc      |
| maize     |
| wheat     |
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:
| party   |
|:--------|
| bjp     |
| inc     |
| ysrcp   |
| jmm     |
| cpi(m)  |
| ss      |
| npp     |
| mnf     |
| ndpp    |
| bjd     |
| skm     |
| dmk     |
| trs     |
| aitc    |
| aap     |
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:
| nationality   |
|:--------------|
| gbr           |
| aus           |
| den           |
| pol           |
| esp           |
| tha           |
| jpn           |
| usa           |
| swe           |
| hkg           |
| chn           |
| ger           |
| fra           |
| sui           |
| aut           |
| irl           |
| por           |
| bel           |
| nzl           |
| nor           |
| fin           |
| ned           |
| bra           |
| cro           |
| arg           |
| sgp           |
| ita           |
| can           |
| rus           |
| cze           |
| per           |
| kor           |
| isr           |
| hun           |
| iri           |
| gre           |
| tur           |
| est           |
| mas           |
| slo           |
| cyp           |
| bul           |
| gua           |
| ltu           |
| blr           |
| dom           |
| tpe           |
| esa           |
| uru           |
| oma           |
| tun           |
| pur           |
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:
| due date                |
|:------------------------|
| 2021-05-28 05:30:10.000 |
| 2021-05-24 05:30:10.000 |
| 2021-05-25 05:30:10.000 |
| 2021-05-29 05:30:10.000 |
| 2021-04-27 05:30:10.000 |
| 2021-06-03 05:30:10.000 |
| 2021-06-05 05:30:10.000 |
| 2021-05-06 05:30:10.000 |
| 2021-04-30 05:30:10.000 |
| 2021-11-25 05:30:10.000 |
| 2022-02-23 05:30:10.000 |
| 2022-02-24 05:30:10.000 |
| 2022-04-05 05:30:10.000 |
| 2022-03-30 05:30:10.000 |
| 2022-03-01 05:30:10.000 |
| 2022-03-19 05:30:10.000 |
| 2022-04-09 05:30:10.000 |
| 2022-04-08 05:30:10.000 |
| 2022-03-31 05:30:10.000 |
| 2022-03-29 05:30:10.000 |
| 2022-03-09 05:30:10.000 |
| 2022-10-01 05:30:10.000 |
| 2022-02-19 05:30:10.000 |
| 2021-05-14 05:30:10.000 |
| 2022-03-11 05:30:10.000 |
| 2022-03-21 05:30:10.000 |
| 2022-04-10 05:30:10.000 |
| 2021-05-15 05:30:10.000 |
| 2021-07-02 05:30:10.000 |
| 2021-07-11 05:30:10.000 |
| 2021-07-18 05:30:10.000 |
| 2021-05-13 05:30:10.000 |
| 2021-06-12 05:30:10.000 |
| 2021-06-11 05:30:10.000 |
| 2021-07-14 05:30:10.000 |
| 2021-07-19 05:30:10.000 |
| 2021-04-28 05:30:10.000 |
| 2021-04-23 05:30:10.000 |
| 2021-07-10 05:30:10.000 |
| 2021-05-27 05:30:10.000 |
| 2021-06-08 05:30:10.000 |
| 2021-06-19 05:30:10.000 |
| 2021-05-26 05:30:10.000 |
| 2021-06-24 05:30:10.000 |
| 2021-05-22 05:30:10.000 |
| 2021-04-24 05:30:10.000 |
| 2021-05-10 05:30:10.000 |
| 2023-01-06 05:30:10.000 |
| 2022-03-06 05:30:10.000 |
| 2022-02-27 05:30:10.000 |
| 2023-01-11 05:30:10.000 |
| 2023-03-01 05:30:10.000 |
| 2022-04-15 05:30:10.000 |
| 2021-09-16 05:30:10.000 |
| 2021-09-30 05:30:10.000 |
| 2022-03-02 05:30:10.000 |
| 2021-10-04 05:30:10.000 |
| 2021-04-14 05:30:10.000 |
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:
| textualcoursetypename                |
|:-------------------------------------|
| big data expert                      |
| business intelligence expert         |
| data science professional            |
| cloudera hadoop – big data expert    |
| data analyst expert                  |
| sql basics                           |
| sql server guru dba & data architect |
| power bi                             |
| dba-app                              |
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:
| reporting date          |
|:------------------------|
| 2018-12-01 00:00:00.000 |
| 2018-12-05 00:00:00.000 |
| 2018-12-19 00:00:00.000 |
| 2019-01-15 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:
| month name   |
|:-------------|
| may          |
| june         |
| october      |
| january      |
| april        |
| september    |
| november     |
| july         |
| febuary      |
| august       |
| march        |
| december     |


label: febuary
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:
| week                    |
|:------------------------|
| 2019-01-13 00:00:00.000 |
| 2019-01-20 00:00:00.000 |
| 2019-01-27 00:00:00.000 |
| 2019-02-03 00:00:00.000 |
| 2019-02-10 00:00:00.000 |
| 2019-02-17 00:00:00.000 |
| 2019-02-24 00:00:00.000 |
| 2019-03-03 00:00:00.000 |
| 2019-03-10 00:00:00.000 |
| 2019-03-17 00:00:00.000 |
| 2019-03-24 00:00:00.000 |
| 2019-03-31 00:00:00.000 |
| 2019-04-07 00:00:00.000 |
| 2019-04-14 00:00:00.000 |
| 2019-04-21 00:00:00.000 |
| 2019-04-28 00:00:00.000 |
| 2019-05-05 00:00:00.000 |
| 2019-05-12 00:00:00.000 |
| 2019-05-19 00:00:00.000 |
| 2019-05-26 00:00:00.000 |
| 2019-06-02 00:00:00.000 |
| 2019-06-09 00:00:00.000 |
| 2019-06-16 00:00:00.000 |
| 2019-06-23 00:00:00.000 |
| 2019-06-30 00:00:00.000 |
| 2019-07-07 00:00:00.000 |
| 2019-07-14 00:00:00.000 |
| 2019-07-21 00:00:00.000 |
| 2019-07-28 00:00:00.000 |
| 2019-08-04 00:00:00.000 |
| 2019-08-11 00:00:00.000 |
| 2019-08-18 00:00:00.000 |
| 2019-08-25 00:00:00.000 |
| 2019-09-01 00:00:00.000 |
| 2019-09-08 00:00:00.000 |
| 2019-09-15 00:00:00.000 |
| 2019-09-22 00:00:00.000 |
| 2019-09-29 00:00:00.000 |
| 2019-10-06 00:00:00.000 |
| 2019-10-13 00:00:00.000 |
| 2019-10-20 00:00:00.000 |
| 2019-10-27 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:
| country   |
|:----------|
| dk        |
| fi        |
| br        |
| ca        |
| gr        |
| cr        |
| id        |
| ie        |
| il        |
| it        |
| my        |
| mx        |
| nl        |
| nz        |
| no        |
| pa        |
| ph        |
| pl        |
| pt        |
| pr        |
| qa        |
| rs        |
| sg        |
| es        |
| se        |
| ch        |
| th        |
| tr        |
| ae        |
| gb        |
| us        |
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:
| end time                |
|:------------------------|
| 1899-12-30 19:00:00.000 |
| 1899-12-30 20:00:00.000 |
| 1899-12-30 21:00:00.000 |
| 1899-12-30 13:00:00.000 |
| 1899-12-30 15:00:00.000 |
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:
| date                    |
|:------------------------|
| 2014-10-01 00:00:00.000 |
| 2014-09-01 00:00:00.000 |
| 2014-08-01 00:00:00.000 |
| 2014-07-01 00:00:00.000 |
| 2014-06-01 00:00:00.000 |
| 2014-05-01 00:00:00.000 |
| 2014-04-01 00:00:00.000 |
| 2014-03-01 00:00:00.000 |
| 2014-02-01 00:00:00.000 |
| 2014-01-01 00:00:00.000 |
| 2013-12-01 00:00:00.000 |
| 2013-11-01 00:00:00.000 |
| 2013-10-01 00:00:00.000 |
| 2013-09-01 00:00:00.000 |
| 2013-08-01 00:00:00.000 |
| 2013-07-01 00:00:00.000 |
| 2013-06-01 00:00:00.000 |
| 2013-05-01 00:00:00.000 |
| 2013-04-01 00:00:00.000 |
| 2013-03-01 00:00:00.000 |
| 2013-02-01 00:00:00.000 |
| 2013-01-01 00:00:00.000 |
| 2014-12-01 00:00:00.000 |
| 2014-11-01 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:
| open time               |
|:------------------------|
| 2020-05-18 08:40:00.000 |
| 2020-05-18 06:40:00.000 |
| 2020-05-18 08:20:00.000 |
| 2020-05-19 06:09:00.000 |
| 2020-05-19 09:19:00.000 |
| 2020-05-19 08:40:00.000 |
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:
| time                    |
|:------------------------|
| 1899-12-30 10:00:00.000 |
| 1899-12-30 14:00:00.000 |
| 1899-12-30 08:00:00.000 |
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:
| enttype   |
|:----------|
| d         |
| k         |
| stat      |
| ftg       |
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:
| fy   |
|:-----|
| fy19 |
| fy20 |
| fy21 |
| fy22 |
| fy23 |
| fy24 |
| fy25 |
| fy26 |
| fy27 |
| fy28 |
| fy29 |
| fy30 |
| fy31 |
| fy32 |
| fy33 |
| fy34 |
| fy35 |
| fy36 |
| fy37 |
| fy38 |
| fy39 |
| fy40 |
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:
| calculation   |
|:--------------|
| sum           |
| blank         |
| calculation   |
| bridge        |
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:
| color   |
|:--------|
| yellow  |
| blue    |
| black   |
| silver  |
| red     |
| white   |
| multi   |
| grey    |
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:
| yearmonthnumber   |
|:------------------|
| 2015/07           |
| 2015/08           |
| 2015/09           |
| 2016/07           |
| 2016/08           |
| 2016/09           |
| 2016/10           |
| 2016/11           |
| 2016/12           |
| 2015/10           |
| 2015/11           |
| 2015/12           |
| 2015/01           |
| 2015/02           |
| 2015/03           |
| 2016/01           |
| 2016/02           |
| 2016/03           |
| 2016/04           |
| 2016/05           |
| 2016/06           |
| 2015/04           |
| 2015/05           |
| 2015/06           |
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:
| country   |
|:----------|
| ro        |
| tr        |
| sr        |
| bg        |
| mn        |
| mc        |
| it        |
| hu        |
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   |
|:-------------|
| sep-2018     |
| nov-2018     |
| jan-2019     |
| oct-2018     |
| mar-2020     |
| jan-2020     |
| apr-2020     |
| feb-2020     |
| dec-2019     |
| may-2019     |
| aug-2019     |
| mar-2019     |
| apr-2019     |
| dec-2018     |
| feb-2019     |
| nov-2019     |
| oct-2019     |
| sep-2019     |
| jul-2019     |
| aug-2018     |
| jun-2019     |
| feb-2018     |
| jul-2018     |
| apr-2018     |
| may-2018     |
| mar-2018     |
| jun-2018     |
| jan-2018     |
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:
| hiredate                |
|:------------------------|
| 2016-12-19 00:00:00.000 |
| 2015-12-21 00:00:00.000 |
| 2018-03-03 00:00:00.000 |
| 2020-01-27 00:00:00.000 |
| 2019-12-11 00:00:00.000 |
| 2021-07-19 00:00:00.000 |
| 2019-08-05 00:00:00.000 |
| 2019-07-27 00:00:00.000 |
| 2020-03-17 00:00:00.000 |
| 2016-01-17 00:00:00.000 |
| 2016-10-30 00:00:00.000 |
| 2018-05-29 00:00:00.000 |
| 2019-09-30 00:00:00.000 |
| 2020-02-15 00:00:00.000 |
| 2015-06-25 00:00:00.000 |
| 2020-06-27 00:00:00.000 |
| 2018-10-20 00:00:00.000 |
| 2020-02-22 00:00:00.000 |
| 2015-11-14 00:00:00.000 |
| 2019-08-11 00:00:00.000 |
| 2018-08-23 00:00:00.000 |
| 2017-01-19 00:00:00.000 |
| 2020-09-10 00:00:00.000 |
| 2021-01-16 00:00:00.000 |
| 2020-01-10 00:00:00.000 |
| 2021-01-02 00:00:00.000 |
| 2017-04-04 00:00:00.000 |
| 2021-01-04 00:00:00.000 |
| 2016-08-22 00:00:00.000 |
| 2016-05-03 00:00:00.000 |
| 2016-01-09 00:00:00.000 |
| 2019-11-10 00:00:00.000 |
| 2015-05-09 00:00:00.000 |
| 2018-03-28 00:00:00.000 |
| 2020-10-21 00:00:00.000 |
| 2017-03-10 00:00:00.000 |
| 2019-06-21 00:00:00.000 |
| 2015-10-03 00:00:00.000 |
| 2018-02-04 00:00:00.000 |
| 2020-05-21 00:00:00.000 |
| 2017-01-02 00:00:00.000 |
| 2020-02-19 00:00:00.000 |
| 2018-02-21 00:00:00.000 |
| 2017-02-28 00:00:00.000 |
| 2017-07-29 00:00:00.000 |
| 2018-04-04 00:00:00.000 |