Daniel M. Ziegler
E526291
Daniel M. Ziegler is a researcher known for co-authoring influential work in artificial intelligence and machine learning, including large language model research.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Daniel M. Ziegler canonical | 7 |
How this entity was disambiguated
This entity first appeared as the object of triple T4651170 — 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: Daniel M. Ziegler Context triple: [Tom B. Brown, hasCoAuthor, Daniel M. Ziegler]
-
A.
Peter A. Ziegler
Peter A. Ziegler was a prominent Swiss geologist known for his influential work on the tectonic evolution and geological history of Europe.
-
B.
Daniel L. Fapp
Daniel L. Fapp was an American cinematographer known for his work on numerous Hollywood films, including the Oscar-winning West Side Story.
-
C.
Daniel W. Herzog
Daniel W. Herzog is an American Anglican bishop best known for serving as the Bishop of the Episcopal Diocese of Albany in New York.
-
D.
Martin J. Hillenbrand
Martin J. Hillenbrand was an American career diplomat who held several key Cold War-era posts, including serving as U.S. Ambassador to Hungary and later to the Federal Republic of Germany.
-
E.
John Eisendrath
John Eisendrath is a television writer and producer best known for his work on series such as "The Blacklist" and "Alias."
- 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: Daniel M. Ziegler Target entity description: Daniel M. Ziegler is a researcher known for co-authoring influential work in artificial intelligence and machine learning, including large language model research.
-
A.
Peter A. Ziegler
Peter A. Ziegler was a prominent Swiss geologist known for his influential work on the tectonic evolution and geological history of Europe.
-
B.
Daniel L. Fapp
Daniel L. Fapp was an American cinematographer known for his work on numerous Hollywood films, including the Oscar-winning West Side Story.
-
C.
Daniel W. Herzog
Daniel W. Herzog is an American Anglican bishop best known for serving as the Bishop of the Episcopal Diocese of Albany in New York.
-
D.
Martin J. Hillenbrand
Martin J. Hillenbrand was an American career diplomat who held several key Cold War-era posts, including serving as U.S. Ambassador to Hungary and later to the Federal Republic of Germany.
-
E.
John Eisendrath
John Eisendrath is a television writer and producer best known for his work on series such as "The Blacklist" and "Alias."
- F. None of above. chosen
Statements (29)
| Predicate | Object |
|---|---|
| instanceOf |
computer scientist
ⓘ
researcher ⓘ |
| basedIn |
United States
ⓘ
linked to:
United States of America
|
| coAuthorOf |
research on preference-based reinforcement learning for language models
ⓘ
“Fine-Tuning Language Models from Human Preferences” ⓘ |
| collaboratedWith |
OpenAI researchers
ⓘ
academic AI researchers ⓘ |
| degree | Bachelor’s degree in computer science ⓘ |
| educatedAt |
Carnegie Mellon University
ⓘ
linked to:
CMU
|
| employer | OpenAI ⓘ |
| fieldOfWork |
artificial intelligence
ⓘ
large language models ⓘ machine learning ⓘ natural language processing ⓘ |
| hasRole |
AI safety researcher
ⓘ
machine learning engineer ⓘ |
| knownFor |
contributions to alignment techniques for LLMs
ⓘ
work at the intersection of human feedback and language models ⓘ |
| language | English ⓘ |
| notableFor |
co-authoring influential AI research papers
ⓘ
research on large language models ⓘ |
| publicationType |
peer-reviewed conference papers
ⓘ
preprints on arXiv ⓘ |
| researchInterest |
AI alignment
ⓘ
human-AI interaction ⓘ preference learning ⓘ safe deployment of large language models ⓘ |
| workedOn |
language model alignment
ⓘ
reinforcement learning from human feedback ⓘ |
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: Daniel M. Ziegler Description of subject: Daniel M. Ziegler is a researcher known for co-authoring influential work in artificial intelligence and machine learning, including large language model research.
Referenced by (7)
Full triples — surface form annotated when it differs from this entity's canonical label.