Triple

T23142364
Position Surface form Disambiguated ID Type / Status
Subject Essai philosophique sur les probabilités E577495 entity
Predicate influenced P9 FINISHED
Object Bayesian probability — 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: Bayesian probability | Statement: [Essai philosophique sur les probabilités, influenced, Bayesian probability]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bayesian probability
Context triple: [Essai philosophique sur les probabilités, influenced, Bayesian probability]
  • A. Bayesian inference chosen
    Bayesian inference is a statistical framework that updates the probability of hypotheses as more evidence or data becomes available, using Bayes’ theorem to combine prior beliefs with observed information.
  • B. Bayes
    Bayes is a surname most famously associated with Thomas Bayes, the 18th-century statistician and minister whose work led to the development of Bayesian probability theory.
  • C. Bayes’ theorem
    Bayes’ theorem is a fundamental result in probability theory that describes how to update the probability of a hypothesis based on new evidence.
  • D. Bayes rules
    Bayes rules are decision rules in statistical decision theory that minimize expected loss with respect to a prior distribution, forming a central concept in Bayesian optimal decision-making.
  • E. Probability Theory
    Probability Theory is a foundational branch of mathematics that studies random phenomena and quantifies uncertainty using concepts such as probability measures, random variables, and distributions.
  • 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_69e245f8e6248190ba3d58e068b4dccb completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18ecb72fc8190a24e8f5756217a36 completed April 29, 2026, 4:53 a.m.
Created at: April 17, 2026, 4 p.m.