Recognises duplicate data principles
Two principles say the same thing in different words. Readers will hunt for the difference and invent one.
Can AI do this?
Not settled yet The benchmark tested this, but the models' answers do not settle yet whether they master it. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 59% | Not shown |
| gemini-3.5-flash-lite | Low cost | 48% | Not shown |
| gpt-6-luna | Low cost | 61% | Not shown |
| claude-haiku-5-5 | Low cost | 60% | 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 pair and the survivor
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: DP-006 Fix data at source and DP-007 Quality is fixed where data is created Acceptable: One principle; the other retired, superseded_by the survivor
Also known as: Duplicate principles
Related knobits
Builds on
All 25 knobits of Set the data strategy and principles.
Source: Review checklist 01-strategy-principles, row 14 (TOGAF)