Triple
T2631513
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martingale representation theorem |
E59640
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | theorem in stochastic calculus |
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: theorem in stochastic calculus Context triple: [Martingale representation theorem, instanceOf, theorem in stochastic calculus]
-
A.
stochastic process
A stochastic process is a collection of random variables indexed by time or space that describes the evolution of a system subject to inherent randomness.
-
B.
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.
-
C.
result in probability theory
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.
random variable functional
A random variable functional is a mapping that takes one or more random variables (or their distributions) as input and returns a real-valued quantity summarizing some aspect of their probabilistic behavior.
-
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_69ab4ac8596c8190b34997e73d9e991c |
completed | March 6, 2026, 9:44 p.m. |
Created at: March 6, 2026, 9:50 p.m.