Triple
T32308258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cauchy net |
E825424
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | generalization of Cauchy sequence |
C60080
|
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: generalization of Cauchy sequence Context triple: [Cauchy net, instanceOf, generalization of Cauchy sequence]
-
A.
generalized limit
A generalized limit is an extension of the classical notion of limit that assigns “limit-like” values to sequences or functions (often including divergent ones) by relaxing or modifying the usual convergence requirements.
-
B.
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.
-
C.
generalization of Taylor series
A generalization of Taylor series is an expansion technique that represents functions using broader sets of basis functions or more flexible convergence conditions than standard power series, allowing approximation of a wider class of functions.
-
D.
criterion for uniform convergence
A criterion for uniform convergence is a condition or set of conditions that allows one to determine whether a sequence (or series) of functions converges uniformly to a limiting function on a given domain.
-
E.
mathematical constant sequence
A mathematical constant sequence is an ordered list of numbers where each term is the same fixed value, typically representing a specific constant repeated indefinitely.
- 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_69f3491213b88190a57094d8697a7455 |
completed | April 30, 2026, 12:20 p.m. |
Created at: May 1, 2026, 12:45 a.m.