Triple

T22742509
Position Surface form Disambiguated ID Type / Status
Subject Siméon Denis Poisson E562450 entity
Predicate notableWork P4 FINISHED
Object Poisson's law of large numbers formulation — 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's law of large numbers formulation | Statement: [Siméon Denis Poisson, notableWork, Poisson's law of large numbers formulation]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Poisson's law of large numbers formulation
Context triple: [Siméon Denis Poisson, notableWork, Poisson's law of large numbers formulation]
  • A. law of large numbers chosen
    The law of large numbers is a fundamental theorem in probability theory stating that as the number of independent trials increases, the sample average converges to the expected value.
  • B. Erdős–Rényi law of large numbers
    The Erdős–Rényi law of large numbers is a refinement of the classical law of large numbers that provides precise asymptotic behavior and convergence rates for sums of independent random variables, developed by mathematicians Pál Erdős and Alfréd Rényi.
  • C. Kolmogorov strong law of large numbers
    The Kolmogorov strong law of large numbers is a fundamental theorem in probability theory stating that the sample average of independent, identically distributed random variables converges almost surely to their expected value under suitable conditions.
  • D. Gnedenko–Kolmogorov limit theorem
    The Gnedenko–Kolmogorov limit theorem is a fundamental result in probability theory that characterizes the limiting distributions of properly normalized sums of independent random variables, generalizing the classical central limit theorem to stable laws.
  • E. Limit Laws for Sums of Independent Random Variables
    Limit Laws for Sums of Independent Random Variables is a foundational mathematical work that systematically develops the theory of probability limit theorems, including results such as the law of large numbers and central limit behavior for sums of independent random variables.
  • 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.