Triple
T6293639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | law of large numbers |
E141078
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | limit theorem |
C8028
|
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: limit theorem Context triple: [law of large numbers, instanceOf, limit theorem]
-
A.
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.
-
B.
mathematical theorem
A mathematical theorem is a rigorously proven statement derived from axioms and previously established results, expressing a fundamental truth within a formal mathematical system.
-
C.
result in probability theory
chosen
In probability theory, a result is a formally stated and proven fact—such as a theorem, lemma, or corollary—that describes a property or relationship involving probabilistic concepts like random variables, events, or distributions.
-
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.
moment inequality
A moment inequality is a constraint that specifies that an expected value (moment) of a random variable or function of data must lie above or below a certain bound, rather than being exactly equal to it.
- 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_69c008cdf2ac8190bb640c94478fb4ed |
completed | March 22, 2026, 3:20 p.m. |
Created at: March 22, 2026, 4:27 p.m.