Triple

T11398821
Position Surface form Disambiguated ID Type / Status
Subject Daniel Bernoulli E270050 entity
Predicate hasConceptNamedAfter P3325 FINISHED
Object Bernoulli process E141077 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: Bernoulli process | Statement: [Daniel Bernoulli, hasConceptNamedAfter, Bernoulli process]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bernoulli process
Context triple: [Daniel Bernoulli, hasConceptNamedAfter, Bernoulli process]
  • A. Bernoulli trials chosen
    Bernoulli trials are a sequence of independent experiments, each with exactly two possible outcomes (often called success and failure) and the same probability of success on every trial, forming the basis of the binomial distribution in probability theory.
  • B. 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.
  • C. Bernoulli
    Bernoulli is the surname of a prominent Swiss family of mathematicians and scientists, including figures such as Jakob, Johann, and Daniel Bernoulli, who made foundational contributions to calculus, probability, and fluid dynamics.
  • D. 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.
  • E. Markov processes
    Markov processes are stochastic processes in which the future evolution depends only on the present state and not on the past history.
  • 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_69d6aacdbc6c8190af6dc3d5f5d22836 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d8001adc188190ae45227856156412 completed April 9, 2026, 7:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69e58cf75ec08190a571e5178bcde274 completed April 20, 2026, 2:18 a.m.
Created at: April 8, 2026, 9:34 p.m.