Bernhard Romera-Paredes
E736803
Bernhard Romera-Paredes is a machine learning researcher known for his contributions to DeepMind’s work on protein structure prediction, including the development of AlphaFold.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Bernhard Romera-Paredes canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8482649 — 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: Bernhard Romera-Paredes Context triple: [Jumper et al., Nature 2021, hasAuthor, Bernhard Romera-Paredes]
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A.
Bruno Loerzer
Bruno Loerzer was a prominent German First World War fighter ace who later became a high-ranking Luftwaffe general during the Nazi era.
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B.
Carlos Pibernat
Carlos Pibernat was an architect known for designing the headquarters of Banco de la Nación Argentina, a prominent financial institution in the country.
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C.
Bruno Beger
Bruno Beger was a German SS anthropologist and war criminal involved in Nazi racial research and atrocities during World War II.
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D.
Heiner Wilmer
Heiner Wilmer is a German Roman Catholic prelate who serves as the bishop of the Diocese of Hildesheim.
-
E.
Pablo Berger
Pablo Berger is a Spanish film director and screenwriter best known internationally for his acclaimed silent black-and-white film "Blancanieves."
- 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: Bernhard Romera-Paredes Target entity description: Bernhard Romera-Paredes is a machine learning researcher known for his contributions to DeepMind’s work on protein structure prediction, including the development of AlphaFold.
-
A.
Bruno Loerzer
Bruno Loerzer was a prominent German First World War fighter ace who later became a high-ranking Luftwaffe general during the Nazi era.
-
B.
Carlos Pibernat
Carlos Pibernat was an architect known for designing the headquarters of Banco de la Nación Argentina, a prominent financial institution in the country.
-
C.
Bruno Beger
Bruno Beger was a German SS anthropologist and war criminal involved in Nazi racial research and atrocities during World War II.
-
D.
Heiner Wilmer
Heiner Wilmer is a German Roman Catholic prelate who serves as the bishop of the Diocese of Hildesheim.
-
E.
Pablo Berger
Pablo Berger is a Spanish film director and screenwriter best known internationally for his acclaimed silent black-and-white film "Blancanieves."
- F. None of above. chosen
Statements (15)
| Predicate | Object |
|---|---|
| instanceOf |
machine learning researcher
ⓘ
person ⓘ |
| affiliation | DeepMind NERFINISHED ⓘ |
| contributedTo |
AlphaFold protein structure prediction system
NERFINISHED
ⓘ
DeepMind research on protein folding ⓘ |
| countryOfWork | United Kingdom ⓘ |
| employer | Google DeepMind NERFINISHED ⓘ |
| fieldOfWork |
artificial intelligence
ⓘ
machine learning ⓘ protein structure prediction ⓘ |
| gender | male ⓘ |
| knownFor |
contributions to AlphaFold
ⓘ
contributions to DeepMind’s work on protein structure prediction ⓘ |
| notableWork | research on AlphaFold ⓘ |
| worksAt | Google DeepMind NERFINISHED ⓘ |
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: Bernhard Romera-Paredes Description of subject: Bernhard Romera-Paredes is a machine learning researcher known for his contributions to DeepMind’s work on protein structure prediction, including the development of AlphaFold.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.