Triple

T10023590
Position Surface form Disambiguated ID Type / Status
Subject Thomas Bayes E200668 entity
Predicate mathematicalSchool P91765 FINISHED
Object Bayesian school of statistics
The Bayesian school of statistics is an approach to statistical inference that interprets probability as a measure of belief or uncertainty, updating prior beliefs with data via Bayes’ theorem to obtain posterior distributions.
E40249 NE FINISHED

How this triple was built (5 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 school of statistics | Statement: [Thomas Bayes, mathematicalSchool, Bayesian school of statistics]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bayesian school of statistics
Context triple: [Thomas Bayes, mathematicalSchool, Bayesian school of statistics]
  • A. Bayesian inference
    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. Foundations of Statistics
    Foundations of Statistics is a seminal work in statistical theory that rigorously develops the principles of statistical inference and decision-making under uncertainty.
  • C. 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.
  • D. Bayes factor
    The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
  • E. 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.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bayesian school of statistics
Triple: [Thomas Bayes, mathematicalSchool, Bayesian school of statistics]
Generated description
The Bayesian school of statistics is an approach to statistical inference that interprets probability as a measure of belief or uncertainty, updating prior beliefs with data via Bayes’ theorem to obtain posterior distributions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Bayesian school of statistics
Target entity description: The Bayesian school of statistics is an approach to statistical inference that interprets probability as a measure of belief or uncertainty, updating prior beliefs with data via Bayes’ theorem to obtain posterior distributions.
  • 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. Foundations of Statistics
    Foundations of Statistics is a seminal work in statistical theory that rigorously develops the principles of statistical inference and decision-making under uncertainty.
  • C. 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.
  • D. Bayes factor
    The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
  • E. 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.
  • F. None of above.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: mathematicalSchool
Context triple: [Thomas Bayes, mathematicalSchool, Bayesian school of statistics]
  • A. publicSchool
    Indicates that an educational institution is operated and funded by a government or public authority rather than by private entities.
  • B. school
    Indicates that an entity attends, is enrolled in, or is institutionally associated as a student with a particular school.
  • C. educationFacility
    Indicates that one entity functions as an institution or place where the other entity receives or provides education or training.
  • D. educationSystem
    Indicates the relationship in which an entity is part of, governed by, or operates within a particular system or structure of education.
  • E. schoolBelongsTo
    Indicates that a particular school is owned by, affiliated with, or under the authority or jurisdiction of a specific organization, institution, or administrative entity.
  • F. None of above. chosen

Provenance (7 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_69ca831c45f08190ac1505cc15076608 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cdcd7c75548190aa604d90d63dc111 completed April 2, 2026, 1:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69d26abb0ab08190b5bcf101c5680f3c completed April 5, 2026, 1:59 p.m.
NEDg Description generation batch_69d26cc38274819090cf10c2fcf43cc7 completed April 5, 2026, 2:08 p.m.
NED2 Entity disambiguation (via description) batch_69d26d2c91fc8190bc40a678662c19aa completed April 5, 2026, 2:09 p.m.
PD Predicate disambiguation batch_69cd4b7cd4208190b2253583ee2f892c completed April 1, 2026, 4:44 p.m.
PDg Predicate description generation batch_69cd4f8d9b888190b8067bd916dae773 completed April 1, 2026, 5:02 p.m.
Created at: March 30, 2026, 8:53 p.m.