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.