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:
| state   |
|:--------|
| ks      |
| ar      |
| tx      |
| la      |
| ca      |
| id      |
| az      |
| mi      |
| fl      |
| tn      |
| nm      |
| sc      |
| ms      |
| ne      |
| nc      |
| co      |
| al      |
| mo      |
| ok      |
| oh      |
| wi      |
| or      |
| ky      |
| wa      |
| in      |
| nv      |
| ut      |
| il      |
| wy      |
| ga      |
| va      |
| mn      |
| pa      |
| nj      |
| ct      |
| wv      |
| mt      |
| sd      |
| md      |
| ny      |
| ma      |
| ak      |
| me      |
| ia      |
| de      |
| nh      |
| hi      |
| vt      |
| nd      |
| dc      |
| ri      |
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:
| start of quarter        |
|:------------------------|
| 2021-01-01 00:00:00.000 |
| 2021-04-01 00:00:00.000 |
| 2021-07-01 00:00:00.000 |
| 2021-10-01 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:
| item   |
|:-------|
| g      |
| s      |
| h      |
| i      |
| j      |
| e1     |
| f1     |
| g1     |
| s1     |
| h1     |
| i1     |
| j1     |
| e      |
| f      |
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:
| nconcerns about     |
|:--------------------|
| i have concerns     |
| i have religious r  |
| i do not believe it |
| not very big con    |
| because i am pr e   |
| no concerns         |
| i believe that it w |
| abcd                |
| not applicable      |
| i want to be imm    |
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:
| tiername                  |
|:--------------------------|
| coreservices              |
| docker-api-gateway        |
| docker-api-services       |
| offerservice              |
| paymentservice            |
| webtier                   |
| inventory-services        |
| order-processing-services |
| vm_machineagent           |
| web-tier-services         |
| fulfillment-services      |
| confirmationsyncservice   |
| frontend                  |
| movieprocessingrole       |
| paymentsws                |
| fulfillment               |
| inventory                 |
| legacyprocessor           |
| legacytranslation         |
| docker-data-services      |
| docker-data-services-v2   |
| docker-web-tier           |
| commerce                  |
| loginservices             |
| partnerservices           |
| availabilityservice       |
| ecommerce-services        |
| moviesearchsite           |
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                                        |
|:---------------------------------------------|
| black panther                                |
| the avengers                                 |
| the dark knight                              |
| star wars ep. viii the last jedi             |
| finding dory                                 |
| titanic                                      |
| shrek 2                                      |
| star wars ep. i the phantom menace           |
| pirates of the caribbean dead mans chest     |
| toy story 3                                  |
| iron man 3                                   |
| spiderman                                    |
| transformers revenge of the fallen           |
| harry potter and the deathly hallows part ii |
| star wars ep. iii revenge of the sith        |
| finding nemo                                 |
| spiderman 3                                  |
| guardians of the galaxy                      |
| independence day                             |
| harry potter and the sorcerers stone         |
| how the grinch stole christmas               |
| men in black                                 |
| shangchi and the legend of the ten rings     |
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:
| faixa_etaria   |
|:---------------|
| 30-39anos      |
| 10-19anos      |
| sem_info       |
| 20-29anos      |
| 40-49anos      |
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                    |
|:------------------------|
| 2012-11-01 00:00:00.000 |
| 2012-12-01 00:00:00.000 |
| 2012-09-01 00:00:00.000 |
| 2012-10-01 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:
| date                    |
|:------------------------|
| 2018-03-01 00:00:00.000 |
| 2018-04-01 00:00:00.000 |
| 2018-05-01 00:00:00.000 |
| 2018-06-01 00:00:00.000 |
| 2018-07-01 00:00:00.000 |
| 2018-08-01 00:00:00.000 |
| 2018-09-01 00:00:00.000 |
| 2018-10-01 00:00:00.000 |
| 2018-11-01 00:00:00.000 |
| 2018-12-01 00:00:00.000 |
| 2019-01-01 00:00:00.000 |
| 2019-02-01 00:00:00.000 |
| 2019-03-01 00:00:00.000 |
| 2019-04-01 00:00:00.000 |
| 2018-01-01 00:00:00.000 |
| 2018-02-01 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:
| date                    |
|:------------------------|
| 2020-08-13 00:00:00.000 |
| 2020-08-14 00:00:00.000 |
| 2020-08-15 00:00:00.000 |
| 2020-08-16 00:00:00.000 |
| 2020-08-17 00:00:00.000 |
| 2020-08-18 00:00:00.000 |
| 2020-08-19 00:00:00.000 |
| 2020-08-20 00:00:00.000 |
| 2020-08-21 00:00:00.000 |
| 2020-08-22 00:00:00.000 |
| 2020-08-23 00:00:00.000 |
| 2020-08-24 00:00:00.000 |
| 2020-08-25 00:00:00.000 |
| 2020-08-26 00:00:00.000 |
| 2020-08-27 00:00:00.000 |
| 2020-08-28 00:00:00.000 |
| 2020-08-29 00:00:00.000 |
| 2020-08-30 00:00:00.000 |
| 2020-08-31 00:00:00.000 |
| 2020-09-01 00:00:00.000 |
| 2020-09-02 00:00:00.000 |
| 2020-09-03 00:00:00.000 |
| 2020-09-04 00:00:00.000 |
| 2020-09-05 00:00:00.000 |
| 2020-09-06 00:00:00.000 |
| 2020-09-07 00:00:00.000 |
| 2020-09-08 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:
| creationdate_project    |
|:------------------------|
| 2018-02-19 00:00:00.000 |
| 2018-02-27 00:00:00.000 |
| 2018-03-22 00:00:00.000 |
| 2020-01-13 00:00:00.000 |
| 2020-04-24 00:00:00.000 |
| 2020-10-13 00:00:00.000 |
| 2020-09-07 00:00:00.000 |
| 2018-02-21 00:00:00.000 |
| 2018-03-06 00:00:00.000 |
| 2018-04-16 00:00:00.000 |
| 2018-05-09 00:00:00.000 |
| 2018-10-22 00:00:00.000 |
| 2018-10-26 00:00:00.000 |
| 2018-11-22 00:00:00.000 |
| 2018-11-27 00:00:00.000 |
| 2019-04-24 00:00:00.000 |
| 2019-05-03 00:00:00.000 |
| 2019-05-23 00:00:00.000 |
| 2019-09-10 00:00:00.000 |
| 2019-09-24 00:00:00.000 |
| 2020-03-30 00:00:00.000 |
| 2020-03-31 00:00:00.000 |
| 2020-05-06 00:00:00.000 |
| 2020-05-13 00:00:00.000 |
| 2020-05-29 00:00:00.000 |
| 2020-06-02 00:00:00.000 |
| 2020-06-08 00:00:00.000 |
| 2020-06-29 00:00:00.000 |
| 2020-09-17 00:00:00.000 |
| 2020-11-09 00:00:00.000 |
| 2020-11-23 00:00:00.000 |
| 2018-02-22 00:00:00.000 |
| 2018-05-14 00:00:00.000 |
| 2018-05-28 00:00:00.000 |
| 2018-10-29 00:00:00.000 |
| 2019-01-04 00:00:00.000 |
| 2019-03-15 00:00:00.000 |
| 2019-07-03 00:00:00.000 |
| 2019-09-05 00:00:00.000 |
| 2019-11-05 00:00:00.000 |
| 2019-12-04 00:00:00.000 |
| 2020-01-07 00:00:00.000 |
| 2020-01-21 00:00:00.000 |
| 2020-10-06 00:00:00.000 |
| 2020-11-18 00:00:00.000 |
| 2020-11-26 00:00:00.000 |
| 2021-01-20 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:
| names   |
|:--------|
| gary    |
| luke    |
| james   |
| richard |
| connor  |
| sian    |
| hannah  |
| jimmy   |
| sean    |
| paul    |
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:
| column9       |
|:--------------|
| deaths 282    |
| deaths 6,206  |
| deaths 12,136 |
| deaths 1,087  |
| deaths 13,654 |
| deaths 4      |
| deaths 25,363 |
| deaths 3,557  |
| deaths 10,159 |
| deaths 10,104 |
| deaths 3,891  |
| deaths 4,567  |
| deaths 5,203  |
| deaths 38,431 |
| deaths 50,832 |
| deaths 222    |
| deaths 51     |
| deaths 10,545 |
| deaths 2,015  |
| deaths 1,490  |
| deaths 570    |
| deaths 703    |
| deaths 8,481  |
| deaths 1,887  |
| deaths 16,769 |
| deaths 9,005  |
| deaths 413    |
| deaths 36,989 |
| deaths 4,057  |
| deaths 840    |
| deaths 22,963 |
| deaths 7,440  |
| deaths 20,088 |
| deaths 129    |
| deaths 14,510 |
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:
| event   |
|:--------|
| zz      |
| ww      |
| xx      |
| yy      |
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                    |
|:------------------------|
| 2017-07-24 00:00:00.000 |
| 2017-07-20 00:00:00.000 |
| 2017-07-21 00:00:00.000 |
| 2017-07-22 00:00:00.000 |
| 2017-07-23 00:00:00.000 |
| 2017-07-25 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:
| abuser descr   |
|:---------------|
| low risk       |
| average risk   |
| high risk      |
| medium risk    |
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 code   |
|:----------------|
| white           |
| yellow          |
| blue            |
| silver          |
| green           |
| red             |
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:
| companiesphones   |
|:------------------|
| (22) 6539-2114    |
| (59) 9922-2903    |
| (47) 2627-1232    |
| (40) 9097-1880    |
| (75) 2366-6489    |
| (49) 4738-0037    |
| (87) 3407-9858    |
| (66) 9905-5683    |
| (94) 6629-0692    |
| (38) 6993-2117    |
| (72) 5635-8839    |
| (72) 4719-4897    |
| (22) 6993-0124    |
| (41) 7717-5906    |
| (38) 7305-7060    |
| (95) 6644-4079    |
| (76) 6623-7360    |
| (42) 5023-2241    |
| (54) 9106-6092    |
| (65) 9597-3887    |
| (77) 6402-7529    |
| (92) 3222-1598    |
| (27) 9925-0134    |
| (15) 5885-9858    |
| (43) 3282-2924    |
| (53) 5339-6704    |
| (78) 8562-5007    |
| (67) 2707-8203    |
| (41) 9635-0575    |
| (47) 9285-3910    |
| (15) 6518-6603    |
| (91) 2727-6856    |
| (61) 4425-1329    |
| (78) 4464-7756    |
| (23) 1443-9130    |
| (94) 6570-4540    |
| (54) 4294-8441    |
| (62) 4022-2429    |
| (29) 3528-8592    |
| (72) 4078-7452    |
| (12) 8805-7156    |
| (52) 8996-5447    |
| (22) 1871-5941    |
| (38) 6887-5457    |
| (83) 7290-0762    |
| (65) 7785-3262    |
| (68) 1448-0965    |
| (30) 1459-0708    |
| (97) 7102-6335    |
| (43) 3527-5103    |
| (82) 8130-9750    |
| (28) 6427-6859    |
| (57) 5432-3347    |
| (99) 8172-1982    |
| (64) 3547-7026    |
| (56) 1409-6352    |
| (83) 6853-2834    |
| (55) 8140-2583    |
| (58) 7781-8655    |
| (18) 9055-4255    |
| (60) 6556-2043    |
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:
| job_industry_category   |
|:------------------------|
| manufacturing           |
| telecommunications      |
| health                  |
| financial services      |
| retail                  |
| argiculture             |
| property                |
| it                      |
| entertainment           |


label: argiculture
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:
| attribute   |
|:------------|
| impressions |
| revenue     |
| requests    |
| units       |
| sessions    |
| users       |
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:
| account   |
|:----------|
| c0002     |
| w9998     |
| a0023     |
| l0394     |
| c0001     |
| i0377     |
| i0052     |
| w9999     |
| k0057     |
| m0521     |
| w4445     |
| j0002     |
| f0083     |
| w5222     |
| m0531     |
| a0709     |
| c0011     |
| i0001     |
| c0004     |
| g1100     |
| i1152     |
| d0481     |
| g1300     |
| i0002     |
| w2222     |
| b0375     |
| w5558     |
| f0081     |
| i1154     |
| c0003     |
| d0320     |
| f0047     |
| f0008     |
| j0332     |
| i0354     |
| w0396     |
| i0919     |
| i1119     |
| w0999     |
| i0055     |
| i1503     |
| g1200     |
| g2100     |
| i0053     |
| i0372     |
| i0477     |
| c0009     |
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:
| year                    |
|:------------------------|
| 2019-01-01 00:00:00.000 |
| 2020-01-01 00:00:00.000 |
| 2021-01-01 00:00:00.000 |
| 2022-01-01 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:
| closed_by                          |
|:-----------------------------------|
| sravani potala                     |
| venkata rakesh annapureddy         |
| muhammad jasim                     |
| arunkumar kolakaluri               |
| vijay baskar jayabalan             |
| suma oggisetty                     |
| deepa krishnamoorthy               |
| ntm serviceuser                    |
| monica reena rai                   |
| santhosh kumaran duraisamy         |
| sibiprasanth sakthivel             |
| saiteja guvvala                    |
| prabal malla                       |
| philip wilhelm                     |
| anukirthika uthama ravi chandran   |
| kaarthikeyan raghupathy            |
| ranesh arumugam gokulasundar kumar |
| curtis cologna                     |
| charles tustison                   |
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:
| entity   |
|:---------|
| 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:
| dayofweekname   |
|:----------------|
| friday          |
| thursday        |
| wednesday       |
| tuesday         |
| monday          |
| saturday        |
| sunday          |
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_code   |
|:-----------------|
| an               |
| af               |
| oc               |
| eu               |
| as               |
| sa               |
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        |
|:---------------|
| czech republic |
| ireland        |
| finland        |
| france         |
| russia         |
| spain          |
| portugal       |
| germany        |
| scotland       |
| denmark        |
| sweden         |
| poland         |
| united kingdom |
| switzerland    |
| belgium        |
| ukraine        |
| hungary        |
| austria        |
| italy          |
| netherlands    |
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:
| trimestre   |
|:------------|
| trim. 1     |
| trim. 2     |
| trim. 3     |
| trim. 4     |
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:
| new_saleslookup                      |
|:-------------------------------------|
| dcf3f26c-8dae-e711-8112-e0071b667141 |
| af10615f-b49c-e811-a851-000d3ab54165 |
| 494f1727-8dae-e711-8113-e0071b66f061 |
| 949c30e0-a6ec-e811-a859-000d3ab54165 |
| 3e317b6a-6942-e411-ab17-d89d6764969c |
| 2033d86c-3989-e911-a868-000d3ab54165 |
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     |
|:----------|
| december  |
| april     |
| october   |
| june      |
| january   |
| november  |
| july      |
| september |
| may       |
| march     |
| august    |
| february  |
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:
| product_id   |
|:-------------|
| ii           |
| rd           |
| rm           |
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                     |
|:-------------------------|
| autodoor accessories     |
| autodoor motor           |
| autodoor shaft           |
| autogate boom            |
| autogate control         |
| autogate rack            |
| autogate safety          |
| autogate slide           |
| autogate slide pro       |
| autogate spare           |
| autogate swing           |
| autogate swing pro       |
| gatefit cantilever       |
| gatefit latch            |
| gatefit pedestrian swing |
| gatefit slide            |
| gatefit swing            |
| rural fittings           |
| safety gates             |
| sliding track bracket    |
| flat                     |
| sliding track hanger     |
| sliding track            |
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   |
|:--------|
| apr-21  |
| aug-20  |
| aug-21  |
| dec-20  |
| dec-21  |
| feb-21  |
| jan-21  |
| jan-22  |
| jul-20  |
| jul-21  |
| jun-20  |
| jun-21  |
| mar-21  |
| may-21  |
| nov-20  |
| nov-21  |
| oct-20  |
| oct-21  |
| sep-20  |
| sep-21  |
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:
| event start             |
|:------------------------|
| 2022-02-09 17:28:19.033 |
| 2022-02-09 17:28:21.393 |
| 2022-02-09 17:28:31.743 |
| 2022-02-09 17:28:38.646 |
| 2022-02-09 17:28:43.323 |
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:
| fiscal month            |
|:------------------------|
| 2015-12-01 00:00:00.000 |
| 2016-01-01 00:00:00.000 |
| 2016-02-01 00:00:00.000 |
| 2016-03-01 00:00:00.000 |
| 2016-04-01 00:00:00.000 |
| 2016-05-01 00:00:00.000 |
| 2016-06-01 00:00:00.000 |
| 2016-07-01 00:00:00.000 |
| 2016-08-01 00:00:00.000 |
| 2016-09-01 00:00:00.000 |
| 2016-10-01 00:00:00.000 |
| 2016-11-01 00:00:00.000 |
| 2016-12-01 00:00:00.000 |
| 2017-01-01 00:00:00.000 |
| 2017-02-01 00:00:00.000 |
| 2017-03-01 00:00:00.000 |
| 2017-04-01 00:00:00.000 |
| 2017-05-01 00:00:00.000 |
| 2017-06-01 00:00:00.000 |
| 2017-07-01 00:00:00.000 |
| 2017-08-01 00:00:00.000 |
| 2017-09-01 00:00:00.000 |
| 2017-10-01 00:00:00.000 |
| 2017-11-01 00:00:00.000 |
| 2017-12-01 00:00:00.000 |
| 2018-01-01 00:00:00.000 |
| 2018-02-01 00:00:00.000 |
| 2018-03-01 00:00:00.000 |
| 2018-04-01 00:00:00.000 |
| 2018-05-01 00:00:00.000 |
| 2018-06-01 00:00:00.000 |
| 2018-07-01 00:00:00.000 |
| 2018-08-01 00:00:00.000 |
| 2018-09-01 00:00:00.000 |
| 2018-10-01 00:00:00.000 |
| 2018-11-01 00:00:00.000 |
| 2018-12-01 00:00:00.000 |
| 2019-01-01 00:00:00.000 |
| 2019-02-01 00:00:00.000 |
| 2019-03-01 00:00:00.000 |
| 2019-04-01 00:00:00.000 |
| 2019-05-01 00:00:00.000 |
| 2019-06-01 00:00:00.000 |
| 2019-07-01 00:00:00.000 |
| 2019-08-01 00:00:00.000 |
| 2019-09-01 00:00:00.000 |
| 2019-10-01 00:00:00.000 |
| 2019-11-01 00:00:00.000 |
| 2019-12-01 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:
| eta                     |
|:------------------------|
| 2022-02-24 00:00:00.000 |
| 2022-03-03 00:00:00.000 |
| 2022-03-04 00:00:00.000 |
| 2022-03-02 00:00:00.000 |
| 2022-02-22 00:00:00.000 |
| 2022-02-18 00:00:00.000 |
| 2022-02-11 00:00:00.000 |
| 2022-02-16 00:00:00.000 |
| 2022-02-10 00:00:00.000 |
| 2022-02-07 00:00:00.000 |
| 2022-01-18 00:00:00.000 |
| 2022-01-20 00:00:00.000 |
| 2022-01-31 00:00:00.000 |
| 2022-01-17 00:00:00.000 |
| 2022-01-14 00:00:00.000 |
| 2022-01-13 00:00:00.000 |
| 2022-01-07 00:00:00.000 |
| 2022-01-04 00:00:00.000 |
| 2022-01-05 00:00:00.000 |
| 2021-12-31 00:00:00.000 |
| 2021-12-29 00:00:00.000 |
| 2021-12-24 00:00:00.000 |
| 2022-01-11 00:00:00.000 |
| 2021-12-23 00:00:00.000 |
| 2021-12-30 00:00:00.000 |
| 2021-12-22 00:00:00.000 |
| 2021-12-21 00:00:00.000 |
| 2021-12-14 00:00:00.000 |
| 2021-12-17 00:00:00.000 |
| 2021-12-20 00:00:00.000 |
| 2021-12-15 00:00:00.000 |
| 2021-12-13 00:00:00.000 |
| 2021-12-16 00:00:00.000 |
| 2021-12-10 00:00:00.000 |
| 2021-12-09 00:00:00.000 |