Thomas Bayes
E200668
Thomas Bayes was an 18th-century English statistician and Presbyterian minister best known for formulating Bayes’ theorem, which laid the foundation for Bayesian probability and inference.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Thomas Bayes canonical | 6 |
How this entity was disambiguated
This entity first appeared as the object of triple T1807306 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thomas Bayes Context triple: [Bayesian inference, formalizedBy, Thomas Bayes]
-
A.
Abraham de Moivre
Abraham de Moivre was an 18th-century French mathematician known for his foundational work in probability theory, including early formulations related to the central limit theorem and De Moivre's formula in complex analysis.
-
B.
Pierre-Simon Laplace
Pierre-Simon Laplace was a French mathematician, physicist, and astronomer whose work laid the foundations of celestial mechanics, probability theory, and statistics.
-
C.
John Arbuthnot
John Arbuthnot was an 18th-century Scottish physician, mathematician, and satirist closely associated with Jonathan Swift and Alexander Pope.
-
D.
George Boole
George Boole was a 19th-century English mathematician and logician whose development of Boolean algebra laid the foundations for modern symbolic logic and digital computer circuits.
-
E.
John Napier
John Napier was a Scottish mathematician best known for inventing logarithms and popularizing the use of the decimal point in arithmetic.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Thomas Bayes Target entity description: Thomas Bayes was an 18th-century English statistician and Presbyterian minister best known for formulating Bayes’ theorem, which laid the foundation for Bayesian probability and inference.
-
A.
Abraham de Moivre
Abraham de Moivre was an 18th-century French mathematician known for his foundational work in probability theory, including early formulations related to the central limit theorem and De Moivre's formula in complex analysis.
-
B.
Pierre-Simon Laplace
Pierre-Simon Laplace was a French mathematician, physicist, and astronomer whose work laid the foundations of celestial mechanics, probability theory, and statistics.
-
C.
John Arbuthnot
John Arbuthnot was an 18th-century Scottish physician, mathematician, and satirist closely associated with Jonathan Swift and Alexander Pope.
-
D.
George Boole
George Boole was a 19th-century English mathematician and logician whose development of Boolean algebra laid the foundations for modern symbolic logic and digital computer circuits.
-
E.
John Napier
John Napier was a Scottish mathematician best known for inventing logarithms and popularizing the use of the decimal point in arithmetic.
- F. None of above. chosen
Statements (44)
| Predicate | Object |
|---|---|
| instanceOf |
Presbyterian minister
ⓘ
human ⓘ mathematician ⓘ statistician ⓘ |
| authorOf | An Essay towards solving a Problem in the Doctrine of Chances ⓘ |
| birthYear | 1701 ⓘ |
| birthYearApproximate | true ⓘ |
| contributedTo | foundations of Bayesian statistics ⓘ |
| countryOfCitizenship | Kingdom of Great Britain ⓘ |
| deathDate | 1761-04-17 ⓘ |
| deathYear | 1761 ⓘ |
| era | 18th century ⓘ |
| familyName | Bayes ⓘ |
| fieldOfWork |
mathematics
ⓘ
probability theory ⓘ statistics ⓘ |
| givenName | Thomas ⓘ |
| hasConceptNamedAfter |
Bayesian inference
ⓘ
Bayesian inference ⓘ
surface form:
Bayesian probability
Bayesian inference ⓘ
surface form:
Bayesian statistics
|
| hasTheoremNamedAfter |
Bayes’ theorem
ⓘ
surface form:
Bayes' theorem
|
| historicalPeriod | Age of Enlightenment ⓘ |
| inferredBirthPlace | England ⓘ |
| influenced |
Bayes rules
ⓘ
surface form:
Bayesian decision theory
Bayesian inference ⓘ
surface form:
Bayesian statistics
modern statistical inference ⓘ |
| knownFor |
Bayes’ theorem
ⓘ
surface form:
Bayes' theorem
Bayesian inference ⓘ Bayesian probability ⓘ |
| language | English ⓘ |
| legacy | Bayesian methods in statistics, machine learning, and artificial intelligence ⓘ |
| mathematicalSchool |
Bayesian inference
ⓘ
surface form:
Bayesian school of statistics
|
| memberOf | Presbyterian clergy in England ⓘ |
| name | Thomas Bayes self-link ⓘ |
| nationality | English ⓘ |
| notability | pioneer of Bayesian probability ⓘ |
| notableWork | An Essay towards solving a Problem in the Doctrine of Chances ⓘ |
| occupation |
Presbyterian minister
ⓘ
statistician ⓘ |
| placeOfBurial |
Bunhill Fields
ⓘ
London, England ⓘ
surface form:
London
|
| posthumousPublication | An Essay towards solving a Problem in the Doctrine of Chances ⓘ |
| religion |
Presbyterian
ⓘ
surface form:
Presbyterianism
|
| theologicalTradition | Protestantism ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Thomas Bayes Description of subject: Thomas Bayes was an 18th-century English statistician and Presbyterian minister best known for formulating Bayes’ theorem, which laid the foundation for Bayesian probability and inference.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.