Triple
T34471525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montel space |
E884920
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | locally convex space class |
C61376
|
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: locally convex space class Context triple: [Montel space, instanceOf, locally convex space class]
-
A.
locally convex space
chosen
A locally convex space is a topological vector space whose topology is generated by a family of seminorms, so that every point has a neighborhood base consisting of convex sets.
-
B.
Banach space (for suitable norms)
A Banach space is a vector space over the real or complex numbers equipped with a norm (from a suitable class of norms) such that every Cauchy sequence with respect to that norm converges to a limit within the space.
-
C.
generalization of Lebesgue spaces
A generalization of Lebesgue spaces is a function space framework that extends classical \(L^p\) spaces by relaxing or modifying their integrability, norm, or measure-theoretic structure to capture more nuanced behaviors of functions and distributions.
-
D.
nuclear space
A nuclear space is a topological vector space in which every continuous linear map into an arbitrary Banach space is nuclear (i.e., can be approximated by finite-rank operators with rapidly decaying singular values), giving it strong compactness and approximation properties.
-
E.
topological algebra
A topological algebra is an algebra over a field that is also a topological vector space, where the algebraic operations (addition, scalar multiplication, and multiplication) are continuous with respect to the topology.
- F. None of above.
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_69f349c880408190ade571c471ab154a |
completed | April 30, 2026, 12:23 p.m. |
Created at: May 1, 2026, 2:01 a.m.