Recognises an application-named subject area
The current landscape frozen into the enterprise model.
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 | 74% | Not shown |
| gemini-3.5-flash-lite | Low cost | 63% | Not shown |
| gpt-6-luna | Low cost | 75% | Not shown |
| claude-haiku-5-5 | Low cost | 76% | 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 subject area and the business concepts its content belongs to
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: SA-004 Core Banking Acceptable: Its contents redistributed to Account (balances, holds)
Also known as: Subject area named after an application
Related knobits
Builds on
Usually followed by
All 17 knobits of Build the enterprise data model of subject areas.
Source: Review checklist 02-enterprise-data-model, row 5 (DMBOK)