Triple
T12282774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wiener measure |
E292753
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Gaussian measure |
C30699
|
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: Gaussian measure Context triple: [Wiener measure, instanceOf, Gaussian measure]
-
A.
measure-theoretic construction
A measure-theoretic construction is a rigorous method of building mathematical objects—such as measures, integrals, or probability spaces—by specifying σ-algebras, set functions, and limiting processes that satisfy the axioms of measure theory.
-
B.
Green’s function in Euclidean space
A Green’s function in Euclidean space is a fundamental solution to a linear differential operator that represents the response at one point due to a unit source located at another point, enabling the construction of solutions to boundary value problems via superposition.
-
C.
measure
chosen
A measure is a function that assigns a non-negative extended real number to subsets of a given set in a way that generalizes notions of length, area, and volume while satisfying countable additivity.
-
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.
geometric invariant
A geometric invariant is a property of a geometric object that remains unchanged under a specified group of transformations, such as rotations, translations, or more general symmetries.
- 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
Created at: April 8, 2026, 9:52 p.m.