Triple
T38538475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Whitehead group |
E924762
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | invariant in geometric topology |
C64347
|
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: invariant in geometric topology Context triple: [Whitehead group, instanceOf, invariant in geometric topology]
-
A.
work in geometric topology
Work in geometric topology studies the properties and structures of spaces that are preserved under continuous deformations, focusing on the interplay between geometry and topology in shapes and manifolds.
-
B.
differential-topological invariant
A differential-topological invariant is a quantity or property defined using smooth structures (like differentiable maps, vector fields, or differential forms) that remains unchanged under diffeomorphisms of manifolds.
-
C.
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.
-
D.
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.
-
E.
Laurent polynomial-valued invariant
A Laurent polynomial-valued invariant is a quantity assigned to mathematical objects (such as knots, links, or manifolds) that remains unchanged under specified transformations and takes the form of a Laurent polynomial in one or more variables.
- 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_69f76eadeac081909cdfdd0474cb6765 |
completed | May 3, 2026, 3:50 p.m. |
Created at: May 3, 2026, 4:32 p.m.