Triple
T17752902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poisson distribution with P(s) = e^{-s} |
E443155
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | reference model in spectral statistics |
C26339
|
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: reference model in spectral statistics
Context triple: [Poisson distribution with P(s) = e^{-s}, instanceOf, reference model in spectral statistics]
-
A.
spectroscopic approximation
A spectroscopic approximation is a simplified theoretical or computational model used to estimate spectroscopic properties (such as energy levels, transition frequencies, or intensities) by neglecting or approximating certain physical effects to make calculations tractable.
-
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.
statistical model
chosen
A statistical model is a mathematical representation of observed data and underlying random processes, used to describe relationships, make inferences, and generate predictions.
-
D.
statistical closure theory
Statistical closure theory is a framework in which an infinite hierarchy of statistical moment equations for a complex system is approximated by expressing higher-order moments in terms of lower-order ones, yielding a tractable closed set of equations.
-
E.
statistical framework
A statistical framework is a structured set of principles, assumptions, and methods that guides how data are collected, modeled, analyzed, and interpreted to draw valid inferences about underlying phenomena.
- 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_69d8b9edf16c8190a59ebd245d378f4f |
completed | April 10, 2026, 8:50 a.m. |
Created at: April 10, 2026, 10:10 a.m.