Triple
T11961188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | dominated convergence theorem |
E284672
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | convergence theorem |
C716
|
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: convergence theorem Context triple: [dominated convergence theorem, instanceOf, convergence theorem]
-
A.
criterion for convergence
A criterion for convergence is a specific test or condition used to determine whether a given sequence or series approaches a finite limit as its index or number of terms increases.
-
B.
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.
-
C.
mathematical theorem
chosen
A mathematical theorem is a rigorously proven statement derived from axioms and previously established results, expressing a fundamental truth within a formal mathematical system.
-
D.
central limit theorem
The central limit theorem states that, under broad conditions, the sum (or average) of a large number of independent, identically distributed random variables tends to follow a normal distribution, regardless of the original variables’ distribution.
-
E.
quantitative central limit theorem
The quantitative central limit theorem provides explicit bounds on how quickly the distribution of normalized sums of random variables converges to the normal distribution, typically in terms of metrics like the Kolmogorov or Wasserstein distance.
- 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_69d6ab2eaeb881909f7914758f859413 |
completed | April 8, 2026, 7:23 p.m. |
Created at: April 8, 2026, 9:45 p.m.