Explains persistent identifiers
Identifiers are never reused or renumbered; an entry leaving use is retired with its identifier, not deleted.
Can AI do this?
AI does this well Most of the tested AI models master this. 10 of 10 models master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.1-pro-preview | Flagship | 100% | Mastered |
| gpt-6-astra | Flagship | 100% | Mastered |
| claude-fable-5-1 | Flagship | 100% | Mastered |
| gemini-3.8-flash | Mid tier | 100% | Mastered |
| gpt-6.1-sol | Mid tier | 100% | Mastered |
| claude-sonnet-5-5 | Mid tier | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 100% | Mastered |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | Mastered |
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: Identifiers are never reused or renumbered; an entry leaving use is retired with its identifier, not deleted
How it is assessed: Decides for sample changes whether identity is preserved
Related knobits
Needed for
- K-134 Recognises unstable requirement identifiers
- K-135 Retires a superseded register entry instead of deleting it
- K-423 Recognises a taxonomy node deleted while data still refers to it
- K-424 Recognises a node code reused for a different concept
- K-611 Recognises a surrogate value with meaning, reused or renumbered
- K-916 Explains data model versioning and change management
All 47 knobits of Capture data requirements.
Source: Data-management practice used by the stage 1 reference and review checklist