Triple

T22742493
Position Surface form Disambiguated ID Type / Status
Subject Siméon Denis Poisson E562450 entity
Predicate familyName P18 FINISHED
Object Poisson NE NERFINISHED

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 | Statement: [Siméon Denis Poisson, familyName, Poisson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Poisson
Context triple: [Siméon Denis Poisson, familyName, Poisson]
  • 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-Moulin
    Poisson-Moulin is a small locality within the municipality of Vaux-sur-Sûre in the Walloon region of Belgium.
  • 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. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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.