Triple

T22742501
Position Surface form Disambiguated ID Type / Status
Subject Siméon Denis Poisson E562450 entity
Predicate notableWork P4 FINISHED
Object Poisson distribution NE NERFINISHED

How this triple was built (3 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, notableWork, Poisson distribution]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Poisson distribution
Context triple: [Siméon Denis Poisson, notableWork, Poisson distribution]
  • A. Poisson
    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. Pearson distribution
    The Pearson distribution is a family of continuous probability distributions introduced by Karl Pearson to flexibly model data with varying skewness and kurtosis.
  • E. Bernoulli distribution
    The Bernoulli distribution is a fundamental discrete probability distribution that models a single trial with exactly two possible outcomes, typically labeled success and failure, with a fixed probability of success.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Poisson distribution
Target entity description: The Poisson distribution is a discrete probability distribution that models the number of times an event occurs in a fixed interval of time or space when events happen independently at a constant average rate.
  • A. Poisson
    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 chosen
    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. Pearson distribution
    The Pearson distribution is a family of continuous probability distributions introduced by Karl Pearson to flexibly model data with varying skewness and kurtosis.
  • E. Bernoulli distribution
    The Bernoulli distribution is a fundamental discrete probability distribution that models a single trial with exactly two possible outcomes, typically labeled success and failure, with a fixed probability of success.
  • F. None of above.

Provenance (2 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_69e245513a5c81908d5cb471b4fc429d completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f1797400fc8190bec26726f434f787 completed April 29, 2026, 3:22 a.m.
Created at: April 17, 2026, 3:23 p.m.