Tom Heskes
E838507
Tom Heskes is a machine learning researcher and professor known for his work in probabilistic modeling, Bayesian methods, and neural networks.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Tom Heskes canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T10023613 — 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: Tom Heskes Context triple: [Max Welling, coAuthorWith, Tom Heskes]
-
A.
Jeroen van der Boom
Jeroen van der Boom is a Dutch singer, television presenter, and entertainer known for his successful music career and prominent roles on Dutch TV talent shows.
-
B.
Dennis van Aarssen
Dennis van Aarssen is a Dutch jazz and pop singer who gained national fame after winning the talent show The Voice of Holland.
-
C.
Paul Rijkens
Paul Rijkens was a Dutch businessman and longtime Unilever executive who was instrumental in postwar European integration efforts and co-founded the influential Bilderberg Group.
-
D.
Benno van den Berg
Benno van den Berg is a Dutch mathematician known for his work in category theory, logic, and the foundations of mathematics.
-
E.
Frank Boogaerts
Frank Boogaerts is a Belgian politician who has served as the mayor of the city of Lier in the province of Antwerp.
- 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: Tom Heskes Target entity description: Tom Heskes is a machine learning researcher and professor known for his work in probabilistic modeling, Bayesian methods, and neural networks.
-
A.
Jeroen van der Boom
Jeroen van der Boom is a Dutch singer, television presenter, and entertainer known for his successful music career and prominent roles on Dutch TV talent shows.
-
B.
Dennis van Aarssen
Dennis van Aarssen is a Dutch jazz and pop singer who gained national fame after winning the talent show The Voice of Holland.
-
C.
Paul Rijkens
Paul Rijkens was a Dutch businessman and longtime Unilever executive who was instrumental in postwar European integration efforts and co-founded the influential Bilderberg Group.
-
D.
Benno van den Berg
Benno van den Berg is a Dutch mathematician known for his work in category theory, logic, and the foundations of mathematics.
-
E.
Frank Boogaerts
Frank Boogaerts is a Belgian politician who has served as the mayor of the city of Lier in the province of Antwerp.
- F. None of above. chosen
Statements (29)
| Predicate | Object |
|---|---|
| instanceOf |
human
ⓘ
machine learning researcher ⓘ university professor ⓘ |
| countryOfCitizenship | Netherlands ⓘ |
| employer | Radboud University Nijmegen ⓘ |
| fieldOfWork |
Bayesian methods
ⓘ
artificial intelligence ⓘ computational neuroscience ⓘ machine learning ⓘ neural networks ⓘ probabilistic modeling ⓘ statistics ⓘ |
| hasAcademicPosition | professor ⓘ |
| knownFor |
Bayesian learning methods
ⓘ
approximate inference methods ⓘ graphical models ⓘ probabilistic modeling in machine learning ⓘ research on neural networks ⓘ |
| languageOfWorkOrName |
Dutch
ⓘ
English ⓘ |
| occupation |
computer scientist
ⓘ
researcher ⓘ university teacher ⓘ |
| researchInterest |
Bayesian inference
ⓘ
graphical models ⓘ neural computation ⓘ probabilistic machine learning ⓘ variational methods ⓘ |
| workLocation | Nijmegen ⓘ |
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: Tom Heskes Description of subject: Tom Heskes is a machine learning researcher and professor known for his work in probabilistic modeling, Bayesian methods, and neural networks.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.