Triple
T36467723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tracy–Widom distribution |
E898464
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | distribution in random matrix theory |
C62680
|
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: distribution in random matrix theory Context triple: [Tracy–Widom distribution, instanceOf, distribution in random matrix theory]
-
A.
parameter in random matrix theory
A parameter in random matrix theory is a variable (such as matrix size, symmetry class index, or coupling constant) that controls the statistical properties and limiting behavior of ensembles of random matrices.
-
B.
random matrix ensemble
A random matrix ensemble is a collection of matrices whose entries are random variables specified by a probability distribution, studied to understand the statistical properties of their eigenvalues and eigenvectors.
-
C.
law in random matrix theory
chosen
A law in random matrix theory is a probabilistic rule or theorem that characterizes the limiting distribution or statistical behavior of eigenvalues or related quantities of large random matrices.
-
D.
statistical distribution
A statistical distribution is a conceptual model that describes how the values of a random variable are spread or likely to occur across its possible range.
-
E.
result in probabilistic number theory
A result in probabilistic number theory is a theorem or statement that describes the typical or average behavior of arithmetic objects (such as integers, primes, or multiplicative functions) using probabilistic models and methods.
- 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_69f76e58ebd88190b75d9b169b59d793 |
completed | May 3, 2026, 3:48 p.m. |
Created at: May 3, 2026, 4:10 p.m.