Triple
T5973642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siméon Denis Poisson |
E132932
|
entity |
| Predicate | notableConcept |
P201
|
FINISHED |
| Object | Poisson distribution |
E559801
|
NE FINISHED |
How this triple was built (2 steps)
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.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Poisson distribution | Statement: [Siméon Denis Poisson, notableConcept, Poisson distribution]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Poisson distribution Context triple: [Siméon Denis Poisson, notableConcept, Poisson distribution]
-
A.
Poisson
chosen
Poisson is a French surname most famously associated with Siméon Denis Poisson, a prominent 19th-century mathematician and physicist known for major contributions to probability theory and mathematical physics.
-
B.
Poisson process
The Poisson process is a fundamental stochastic process in probability theory that models random events occurring independently over time or space at a constant average rate.
-
C.
Poisson distribution has P(s) = e^{-s}
The Poisson distribution with P(s) = e^{-s} is a simple statistical model describing uncorrelated, randomly spaced events, often used as a reference for comparison in random matrix theory and spectral statistics.
-
D.
Pareto distribution
The Pareto distribution is a power-law probability distribution often used to model phenomena with heavy tails and strong inequality, such as wealth or city sizes.
-
E.
Dirichlet distribution
The Dirichlet distribution is a family of continuous multivariate probability distributions commonly used as a prior over categorical or multinomial parameters in Bayesian statistics.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
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_69c0086deab081908550159ca23eec9b |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04a01dd4081909097342afff31f9b |
completed | March 22, 2026, 7:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1135b94a88190a9b8a90ecc56cee8 |
completed | March 23, 2026, 10:18 a.m. |
Created at: March 22, 2026, 4:03 p.m.