Triple
T31703449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | q-Gaussian distribution |
E809119
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | generalized Gaussian distribution |
C1604
|
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: generalized Gaussian distribution Context triple: [q-Gaussian distribution, instanceOf, generalized Gaussian distribution]
-
A.
statistical distribution
chosen
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.
-
B.
generalization of Lebesgue spaces
A generalization of Lebesgue spaces is a function space framework that extends classical \(L^p\) spaces by relaxing or modifying their integrability, norm, or measure-theoretic structure to capture more nuanced behaviors of functions and distributions.
-
C.
grain distribution law
A grain distribution law is a conceptual rule or model that describes how grain sizes or quantities are statistically spread within a material, system, or population.
-
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.
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.
- 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_69f348de914081909fc8edff56f34dbe |
completed | April 30, 2026, 12:19 p.m. |
Created at: April 30, 2026, 11:13 p.m.