Triple
T9843642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cauchy convergence criterion |
E239286
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | criterion for convergence |
C26950
|
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: criterion for convergence Context triple: [Cauchy convergence criterion, instanceOf, criterion for convergence]
-
A.
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.
-
B.
criterion in numerical analysis
A criterion in numerical analysis is a quantitative condition or rule—such as a tolerance, convergence test, or stopping condition—used to assess the accuracy, stability, or termination of an algorithm or computational method.
-
C.
necessary conditions for optimality
Necessary conditions for optimality are criteria that any candidate solution must satisfy in order to be considered a potential optimizer (such as a minimum, maximum, or saddle point) of a given objective function under specified constraints.
-
D.
numerical stability condition
A numerical stability condition is a mathematical requirement on the step size, discretization parameters, or algorithmic choices that ensures errors in a numerical method do not grow uncontrollably during computation.
-
E.
optimality conditions
Optimality conditions are mathematical criteria that must be satisfied by a candidate solution to ensure it is a local or global optimum of an optimization problem.
- 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_69ca84e3f0c48190ada72a65ebd50efd |
completed | March 30, 2026, 2:12 p.m. |
Created at: March 30, 2026, 8:33 p.m.