Explains data lineage as a graph of derivations
Nodes for catalogued objects and an edge for each derivation with its transformation logic, read backwards for where a number came from and forwards for what a change breaks (DMBOK2 ch. 12 §4.1; W3C PROV-O).
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 | 100% | Not shown |
| gemini-3.5-flash-lite | Low cost | 100% | Not shown |
| gpt-6-luna | Low cost | 100% | Not shown |
| claude-haiku-5-5 | Low cost | 100% | 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: Nodes for catalogued objects and an edge for each derivation with its transformation logic, read backwards for where a number came from and forwards for what a change breaks (DMBOK2 ch. 12 §4.1; W3C PROV-O).
How it is assessed: Answers a written question on the data lineage correctly
Related knobits
Builds on
Needed for
- M-501 Recognises a broken lineage hop
- M-552 Explains lineage granularity
- M-553 Explains view resolution in lineage
- M-555 Explains source-to-target mappings as lineage edges
All 11 knobits of Trace column-level lineage.
Source: Practice the stage 5 reference and review checklist rest on