Triple
T36876551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Godbillon–Vey invariant |
E911357
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | differential-topological invariant |
C62895
|
CONCEPT FINISHED |
How this triple was built (1 step)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: differential-topological invariant Context triple: [Godbillon–Vey invariant, instanceOf, differential-topological invariant]
-
A.
theory in differential topology
A theory in differential topology is a coherent framework of concepts, theorems, and techniques that studies the properties of smooth manifolds and smooth maps between them that are invariant under smooth deformations.
-
B.
homological invariant
A homological invariant is a quantity or structure derived from homology theory that remains unchanged under specified transformations, used to distinguish and classify mathematical objects up to an appropriate notion of equivalence.
-
C.
example in differential topology
An example in differential topology is a specific smooth manifold or smooth map, often constructed to illustrate or test particular concepts such as smooth structures, embeddings, immersions, or invariants.
-
D.
3-manifold invariant
A 3-manifold invariant is a quantity or structure assigned to a 3-dimensional manifold that remains unchanged under homeomorphisms or diffeomorphisms, used to distinguish and classify such manifolds.
-
E.
geometric invariant
A geometric invariant is a property of a geometric object that remains unchanged under a specified group of transformations, such as rotations, translations, or more general symmetries.
- F. None of above. chosen
Provenance (1 batch)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76e82339881909607a65c0503d941 |
completed | May 3, 2026, 3:49 p.m. |
Created at: May 3, 2026, 4:13 p.m.