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:
| 域名         |
|:-------------|
| comlan       |
| ucssi        |
| sinoage      |
| systex       |
| centerland   |
| nysoftland   |
| gmf-inc      |
| tuling       |
| css          |
| founder      |
| vip.sina     |
| yovole       |
| lenovo       |
| vstecs       |
| cnbmtech     |
| ingrammicro  |
| qiye         |
| digitalchina |
| synnex-grp   |
| xunyisoft    |
| yunstorm     |
| comtech      |
| edensoft     |
| netease      |
| yunwill      |
| gwssi        |
| bespinglobal |
| softwareone  |
| fitgreat     |
| servinet     |
| yungoal      |
| unisvianet   |
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   |
|:------------|
| mpn.3       |
| mpn.4       |
| mpn.5       |
| mpn.1       |
| mpn.2       |
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:
| multifilter                           |
|:--------------------------------------|
| wohngebäude mit blatt:nein            |
| kfz                                   |
| kfz mit blatt:nein                    |
| unfall mit blatt:nein                 |
| rechtsschutz mit blatt:nein           |
| klimastrategie                        |
| klimabilanz                           |
| nachhaltigkeitsstrategie:nein         |
| nachhaltigkeits-/umweltleitlinie:nein |
| esg-kapitalanlage-strategie           |
| positiv-kriterien:nein                |
| engagement:nein                       |
| ausschlüsse                           |
| soziales:nein                         |
| massentierhaltung:nein                |
| konventionelle waffen / rüstung:nein  |
| kernkraftwerke:nein                   |
| tabak:nein                            |
| tierversuche:nein                     |
| pornografie:nein                      |
| stromerzeugung aus kernenergie:nein   |
| haftpflicht mit blatt:nein            |
| hausrat mit blatt:nein                |
| klimabilanz:nein                      |
| esg-kapitalanlage-strategie:nein      |
| positiv-kriterien                     |
| engagement                            |
| kfz:nein                              |
| rechtsschutz:nein                     |
| nachhaltigkeitsstrategie              |
| stromerzeugung aus kernenergie        |
| klimastrategie:nein                   |
| nachhaltigkeits-/umweltleitlinie      |
| soziales                              |
| ausbeuterische kinderarbeit:nein      |
| haftpflicht                           |
| hausrat                               |
| wohngebäude                           |
| unfall                                |
| rechtsschutz                          |
| kohleabbau                            |
| kontroverse waffen                    |
| ausbeuterische kinderarbeit           |
| konventionelle waffen / rüstung       |
| kernkraftwerke                        |
| tabak                                 |
| pornografie                           |
| nachhaltige hausrat                   |
| nachhaltige wohngebäude               |
| tierversuche                          |
| massentierhaltung                     |
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:
| 縣市   |
|:-------|
| 南投縣 |
| 嘉義市 |
| 嘉義縣 |
| 基隆市 |
| 宜蘭縣 |
| 屏東縣 |
| 彰化縣 |
| 新北市 |
| 新竹市 |
| 新竹縣 |
| 桃園市 |
| 澎湖縣 |
| 臺中市 |
| 臺北市 |
| 臺南市 |
| 臺東縣 |
| 花蓮縣 |
| 苗栗縣 |
| 雲林縣 |
| 高雄市 |
| 金門縣 |
| 連江縣 |
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:
| mandateduserkeyusername   |
|:--------------------------|
| linda jenkinson           |
| david j. kim              |
| richard branson           |
| jonathan mason            |
| akulina krylova           |
| jaromir voronoff          |
| sales desk                |
| dorothy w. arner          |
| henry m. mattos           |
| andrew jones              |
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:
| subject   |
|:----------|
| cpaltt01  |
| xtexva01  |
| xtimva01  |
| spastt01  |
| slrtto01  |
| printo01  |
| slrtcr03  |
| mabmm301  |
| lceamn01  |
| odcnpi03  |
| lfemtttt  |
| irstci01  |
| ir3tib01  |
| irltlt01  |
| lrhutttt  |
| b6bltt02  |
| naexkp03  |
| naexkp04  |
| naexkp06  |
| naexkp07  |
| ulqeul01  |
| naexkp01  |
| naexkp02  |
| lorsgprt  |
| lolitoaa  |
| ccusma02  |
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:
| level2                   |
|:-------------------------|
| north america operations |
| european operations      |
| pacific operations       |
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:
| groups   |
|:---------|
| group c  |
| group a  |
| group d  |
| group e  |
| group g  |
| group f  |
| group h  |
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:
| template                                              |
|:------------------------------------------------------|
| et170585                                              |
| et180011                                              |
| et180012                                              |
| et180013                                              |
| et181648                                              |
| et181649                                              |
| et190032                                              |
| et190033                                              |
| email_vivo1_multiplosmodelos_avparc_boletagem_a4_link |
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:
| cargo   |
|:--------|
| loiii   |
| fiv     |
| coiv    |
| opv     |
| coiii   |
| opii    |
| opiv    |
| adii    |
| coii    |
| opi     |
| coi     |
| adi     |
| loii    |
| loi     |
| fiiii   |
| fiii    |
| adiii   |
| loiv    |
| opiii   |
| fii     |
| adv     |
| fiiv    |
| lov     |
| cov     |
| adiv    |
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 + time             |
|:------------------------|
| 2020-09-11 09:24:00.000 |
| 2020-09-11 09:25:00.000 |
| 2020-09-11 09:26:00.000 |
| 2020-09-11 09:28:00.000 |
| 2020-09-11 09:29:00.000 |
| 2020-09-11 09:30:00.000 |
| 2020-09-11 09:32:00.000 |
| 2020-09-11 09:33:00.000 |
| 2020-09-11 09:34:00.000 |
| 2020-09-11 09:35:00.000 |
| 2020-09-11 09:36:00.000 |
| 2020-09-11 09:40:00.000 |
| 2020-09-11 09:41:00.000 |
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:
| customer display name                |
|:-------------------------------------|
| brenda bowden showroom               |
| matter #07                           |
| matter #17                           |
| hamby, shane                         |
| matter #06                           |
| matter #14                           |
| internal law office expenses         |
| accounting                           |
| company meetings                     |
| employee training                    |
| marketing                            |
| proposals                            |
| jake thomas                          |
| matter #19                           |
| jimenez services                     |
| matter #11                           |
| matter #13                           |
| kirby freeman wholesale/distribution |
| matter #09                           |
| matter #15                           |
| peacock, karen                       |
| matter #01                           |
| matter #04                           |
| matter #08                           |
| tingey worldwide freight company     |
| matter #02                           |
| matter #10                           |
| matter #16                           |
| weeks, matt                          |
| matter #05                           |
| willams, cindy                       |


label: willams, cindy
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| category             |
|:---------------------|
| insaat               |
| bireysei krediler    |
| elektrik, gaz, buha  |
| finans ve sigorta f  |
| ulastirma ve depoia  |
| mesleki, billimsel v |
| kamu yonetimi vesa   |
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:
| geb.typ naam   |
|:---------------|
| laagbouw       |
| duplex         |
| hoogbouw       |
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:
| 2级渠道    |
|:-----------|
| 百度sem    |
| 朋友推荐   |
| 淘宝店     |
| olp抓取    |
| 品牌直客   |
| 直营渠道   |
| 百度文库   |
| sem未知    |
| 搜狗sem    |
| 360sem     |
| 百度dsp    |
| 神马sem    |
| qq群抓取   |
| 天猫店     |
| 自营群     |
| 今日头条   |
| 粉丝通     |
| 百度信息流 |
| 沪江sem    |
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:
| order priority   |
|:-----------------|
| not specified    |
| medium           |
| low              |
| critical         |
| high             |
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:
| processing.method         |
|:--------------------------|
| natural / dry             |
| unknown                   |
| washed / wet              |
| semi-washed / semi-pulped |
| other                     |
| pulped natural / honey    |
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:
| enrollment post-status   |
|:-------------------------|
| completed                |
| not attempted            |
| incomplete               |
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:
| 所屬工業區                 |
|:---------------------------|
| 非屬工業區類               |
| 其他工業區                 |
| 台元科技園區               |
| 新竹工業區                 |
| 新竹生物醫學園區           |
| 新豐中崙段工業用地         |
| 芎林工業用地               |
| 新竹科學園區二期(新竹縣)   |
| 新竹科學園區一期(新竹縣)   |
| 科學工業園區第二期(新竹縣) |
| 新竹科學園區一期(新竹市)   |
| 科學工業園區第一期(新竹縣) |
| 新竹科學園區三、五路基地   |
| 新竹科學工業園區第一期     |
| 新竹擴大工業區             |
| 新埔工業用地               |
| 新竹科學園區三期(新竹縣)   |
| 新竹科學園區二期(新竹市)   |
| 園區三、五路基地           |
| 新竹科學工業園區第三期     |
| 所屬工業區                 |
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:
| watershed                      |
|:-------------------------------|
| san juan creek                 |
| santa ana river                |
| aliso creek                    |
| newport bay                    |
| anaheim bay-huntington harbor  |
| laguna coastal streams         |
| coyote creek-san gabriel river |
| newport coastal streams        |
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:
| customer_title       |
|:---------------------|
| lost customer        |
| becoming loyal       |
| high risk customer   |
| potential customer   |
| almost lost customer |
| losing customer      |
| late bloomer         |
| very loyal           |
| platinum customer    |
| recent customer      |
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:
| organization category [organizational culture]   |
|:-------------------------------------------------|
| agree                                            |
| neutral                                          |
| disagree                                         |
| strongly agree                                   |
| strongly disagree                                |
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                    |
|:------------------------|
| 2022-01-04 00:00:00.000 |
| 2022-01-05 00:00:00.000 |
| 2022-01-06 00:00:00.000 |
| 2022-01-07 00:00:00.000 |
| 2022-01-08 00:00:00.000 |
| 2022-01-09 00:00:00.000 |
| 2022-01-10 00:00:00.000 |
| 2022-01-11 00:00:00.000 |
| 2022-01-12 00:00:00.000 |
| 2022-01-13 00:00:00.000 |
| 2022-01-14 00:00:00.000 |
| 2022-01-15 00:00:00.000 |
| 2022-01-16 00:00:00.000 |
| 2022-01-17 00:00:00.000 |
| 2022-01-18 00:00:00.000 |
| 2022-01-19 00:00:00.000 |
| 2022-01-20 00:00:00.000 |
| 2022-01-21 00:00:00.000 |
| 2022-01-22 00:00:00.000 |
| 2022-01-23 00:00:00.000 |
| 2022-01-24 00:00:00.000 |
| 2022-01-25 00:00:00.000 |
| 2022-01-26 00:00:00.000 |
| 2022-01-27 00:00:00.000 |
| 2022-01-28 00:00:00.000 |
| 2022-01-29 00:00:00.000 |
| 2022-01-30 00:00:00.000 |
| 2022-01-31 00:00:00.000 |
| 2022-02-01 00:00:00.000 |
| 2022-02-02 00:00:00.000 |
| 2022-02-03 00:00:00.000 |
| 2022-02-04 00:00:00.000 |
| 2022-02-05 00:00:00.000 |
| 2022-02-06 00:00:00.000 |
| 2022-02-07 00:00:00.000 |
| 2022-02-08 00:00:00.000 |
| 2022-02-09 00:00:00.000 |
| 2022-02-10 00:00:00.000 |
| 2022-02-11 00:00:00.000 |
| 2022-02-12 00:00:00.000 |
| 2022-02-13 00:00:00.000 |
| 2022-02-14 00:00:00.000 |
| 2022-02-15 00:00:00.000 |
| 2022-02-16 00:00:00.000 |
| 2022-02-17 00:00:00.000 |
| 2022-02-18 00:00:00.000 |
| 2022-02-19 00:00:00.000 |
| 2022-02-20 00:00:00.000 |
| 2022-02-21 00:00:00.000 |
| 2022-02-22 00:00:00.000 |
| 2022-02-23 00:00:00.000 |
| 2022-02-24 00:00:00.000 |
| 2022-02-25 00:00:00.000 |
| 2022-02-26 00:00:00.000 |
| 2022-02-27 00:00:00.000 |
| 2022-02-28 00:00:00.000 |
| 2022-03-01 00:00:00.000 |
| 2022-03-02 00:00:00.000 |
| 2022-03-03 00:00:00.000 |
| 2022-03-04 00:00:00.000 |
| 2022-03-05 00:00:00.000 |
| 2022-03-06 00:00:00.000 |
| 2022-03-07 00:00:00.000 |
| 2022-03-08 00:00:00.000 |
| 2022-03-09 00:00:00.000 |
| 2022-03-10 00:00:00.000 |
| 2022-03-11 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:
| monthname   |
|:------------|
| may         |
| july        |
| august      |
| october     |
| december    |
| january     |
| march       |
| april       |
| june        |
| september   |
| november    |
| 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:
| labeng           |
|:-----------------|
| ad - 10555       |
| am - 10050       |
| aa - 10631       |
| al - 10767       |
| ap - 10008       |
| bn - 11061       |
| bh - 11018       |
| cs - east22      |
| ct - 10154       |
| cd - 10817a      |
| dl - 11070       |
| dd - 10251       |
| dl - 11146       |
| dl - 10666       |
| db - 10975       |
| dg - 10323       |
| do - 11075       |
| zeasycare        |
| gl - 10006       |
| ic - 11058       |
| jl - east6       |
| jc - 11054       |
| jo - 10290       |
| zjs              |
| jo - 10174       |
| kj - 10636       |
| lp - 11085       |
| lb - 11056       |
| zps m & l davy   |
| me - 11047       |
| mo - 10609       |
| mb - 10266       |
| mb - 10027       |
| mw - 10547       |
| mr - 10247       |
| nt - 10567       |
| zps other subc   |
| pc - 10754       |
| pw - 11049       |
| pl - 10279       |
| zpollock         |
| zpremierservices |
| rs - 10672       |
| rs - 10254       |
| rb - 10634       |
| rl - 10100       |
| rk - 10822       |
| sg - 10272       |
| sh - 10905       |
| sc - 10322       |
| zps terry group  |
| tg - 10296       |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| region   |
|:---------|
| west     |
| south    |
| east     |
| central  |
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:
| journey start           |
|:------------------------|
| 2018-11-16 00:00:00.000 |
| 2019-03-21 00:00:00.000 |
| 2019-03-26 00:00:00.000 |
| 2019-03-30 00:00:00.000 |
| 2019-02-04 00:00:00.000 |
| 2019-02-09 00:00:00.000 |
| 2019-04-30 00:00:00.000 |
| 2019-05-07 00:00:00.000 |
| 2019-02-10 00:00:00.000 |
| 2019-05-08 00:00:00.000 |
| 2019-02-22 00:00:00.000 |
| 2019-02-21 00:00:00.000 |
| 2019-05-02 00:00:00.000 |
| 2018-10-26 00:00:00.000 |
| 2019-02-13 00:00:00.000 |
| 2019-03-18 00:00:00.000 |
| 2019-02-11 00:00:00.000 |
| 2019-02-20 00:00:00.000 |
| 2017-11-30 00:00:00.000 |
| 2019-04-07 00:00:00.000 |
| 2019-05-03 00:00:00.000 |
| 2017-02-03 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:
| end                     |
|:------------------------|
| 2022-04-07 21:20:00.000 |
| 2022-04-08 15:51:00.000 |
| 2022-04-08 08:31:00.000 |
| 2022-04-08 01:38:00.000 |
| 2022-04-09 02:56:00.000 |
| 2022-04-10 07:23:00.000 |
| 2022-04-10 15:43:00.000 |
| 2022-04-10 13:41:00.000 |
| 2022-04-11 05:00:00.000 |
| 2022-04-11 06:59:00.000 |
| 2022-04-11 00:51:00.000 |
| 2022-04-10 05:19:00.000 |
| 2022-04-11 02:04:00.000 |
| 2022-04-11 02:07:00.000 |
| 2022-04-11 02:43:00.000 |
| 2022-04-11 09:09:00.000 |
| 2022-04-11 06:10:00.000 |
| 2022-04-11 19:32:00.000 |
| 2022-04-10 09:24:00.000 |
| 2022-04-11 04:04:00.000 |
| 2022-04-10 20:36:00.000 |
| 2022-04-11 03:32:00.000 |
| 2022-04-10 11:16:00.000 |
| 2022-04-09 12:53:00.000 |
| 2022-04-10 23:34:00.000 |
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                          |
|:-----------------------------------|
| saravanan sivakumar                |
| richard rana                       |
| gopikrishna telakapalli            |
| aleemullah shaik                   |
| santhosh goud mothkuri             |
| ramu nagalla                       |
| nagarjuna ganji                    |
| rakesh mondal                      |
| anto thomson                       |
| karthik nalla                      |
| kishore ghanta                     |
| ntm serviceuser                    |
| suma oggisetty                     |
| arunkumar kolakaluri               |
| muhammad jasim                     |
| philip wilhelm                     |
| vijay baskar jayabalan             |
| sravani potala                     |
| deepa krishnamoorthy               |
| monica reena rai                   |
| saiteja guvvala                    |
| santhosh kumaran duraisamy         |
| curtis cologna                     |
| anand mohan                        |
| sam bolinger                       |
| kaarthikeyan raghupathy            |
| ranesh arumugam gokulasundar kumar |
| brian pederslie                    |
| sibiprasanth sakthivel             |
| craig dayton                       |
| prabal malla                       |
| charles tustison                   |
| venkata rakesh annapureddy         |
| luke medcalf                       |
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:
| diet      |
|:----------|
| carnivore |
| omnivore  |
| herbivore |
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:
| inco terms   |
|:-------------|
| cif          |
| fob          |
| dap          |
| cip          |
| dat          |
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:
| dealdirection   |
|:----------------|
| deposit         |
| sell            |
| buy             |
| loan            |
| holding         |
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:
| 3_point_shooting_categories   |
|:------------------------------|
| avarge 3pt shooting game      |
| good  3pt shooting game       |
| bad 3pt shooting game         |


label: avarge 3pt shooting game
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:
| delay type           |
|:---------------------|
| 5070_minestar system |
| hydraulic systems    |
| main structure       |
| weekly inspection    |
| engine               |
| 5070_no fault found  |
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   |
|:------------|
| mm          |
| r 2016.012  |
| iris        |
| r 2020.03   |
| vrg         |