Recognises duplicate data quality issues left unlinked
Two issues of one cause, logged separately.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 89% | Not shown |
| gemini-3.5-flash-lite | Low cost | 83% | Not shown |
| gpt-6-luna | Low cost | 83% | Not shown |
| claude-haiku-5-5 | Low cost | 83% | Not shown |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: The duplicate linked to the master issue
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: IS-005 "NPL ratio looks low" (Finance) not linked to IS-001 Acceptable: IS-005 duplicate_of: IS-001
Also known as: Duplicate issues not linked
Related knobits
Builds on
All 9 knobits of Manage data quality issues.
Source: Review checklist 06-issue-management, row 4 (ITIL; DMBOK)