Triple

T10023571

Position	Surface form	Disambiguated ID	Type / Status
Subject	Thomas Bayes	`E200668`	entity
Predicate	notableWork	`P4`	FINISHED
Object	An Essay towards solving a Problem in the Doctrine of Chances "An Essay towards solving a Problem in the Doctrine of Chances" is the posthumously published paper by Thomas Bayes that introduced the foundational ideas of Bayesian probability theory.	`E835243`	NE FINISHED

How this triple was built (4 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: An Essay towards solving a Problem in the Doctrine of Chances | Statement: [Thomas Bayes, notableWork, An Essay towards solving a Problem in the Doctrine of Chances]

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: An Essay towards solving a Problem in the Doctrine of Chances
Context triple: [Thomas Bayes, notableWork, An Essay towards solving a Problem in the Doctrine of Chances]

A. The Doctrine of Chances
The Doctrine of Chances is an influential 18th-century treatise by Abraham de Moivre that systematically developed the mathematical theory of probability, especially as applied to games of chance.
B. Ars Conjectandi
Ars Conjectandi is a foundational 1713 treatise on probability theory by Jakob Bernoulli that systematically developed the mathematical study of chance and introduced key concepts such as the law of large numbers.
C. A Treatise on Probability
A Treatise on Probability is John Maynard Keynes’s influential 1921 work that develops a logical and philosophical theory of probability, challenging classical and frequency-based interpretations.
D. De ratiociniis in ludo aleae
De ratiociniis in ludo aleae is a pioneering 17th-century treatise on probability theory, particularly as applied to games of chance.
E. The Theory of Probability
The Theory of Probability is Hans Reichenbach’s influential philosophical and mathematical treatise that helped establish a rigorous, frequency-based interpretation of probability within the logical empiricist tradition.
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: An Essay towards solving a Problem in the Doctrine of Chances
Triple: [Thomas Bayes, notableWork, An Essay towards solving a Problem in the Doctrine of Chances]

Generated description

"An Essay towards solving a Problem in the Doctrine of Chances" is the posthumously published paper by Thomas Bayes that introduced the foundational ideas of Bayesian probability theory.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: An Essay towards solving a Problem in the Doctrine of Chances
Target entity description: "An Essay towards solving a Problem in the Doctrine of Chances" is the posthumously published paper by Thomas Bayes that introduced the foundational ideas of Bayesian probability theory.

A. The Doctrine of Chances
The Doctrine of Chances is an influential 18th-century treatise by Abraham de Moivre that systematically developed the mathematical theory of probability, especially as applied to games of chance.
B. Ars Conjectandi
Ars Conjectandi is a foundational 1713 treatise on probability theory by Jakob Bernoulli that systematically developed the mathematical study of chance and introduced key concepts such as the law of large numbers.
C. A Treatise on Probability
A Treatise on Probability is John Maynard Keynes’s influential 1921 work that develops a logical and philosophical theory of probability, challenging classical and frequency-based interpretations.
D. De ratiociniis in ludo aleae
De ratiociniis in ludo aleae is a pioneering 17th-century treatise on probability theory, particularly as applied to games of chance.
E. The Theory of Probability
The Theory of Probability is Hans Reichenbach’s influential philosophical and mathematical treatise that helped establish a rigorous, frequency-based interpretation of probability within the logical empiricist tradition.
F. None of above. chosen

Provenance (5 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.

Created at: March 30, 2026, 8:53 p.m.