John Jumper
E736795
John Jumper is a computational biologist best known as the lead researcher behind DeepMind’s AlphaFold, the breakthrough AI system for predicting protein structures.
All labels observed (1)
| Label | Occurrences |
|---|---|
| John Jumper canonical | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T8482633 — 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.
Target entity: John Jumper Context triple: [Jumper et al., Nature 2021, hasFirstAuthor, John Jumper]
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A.
John Bowers
John Bowers is a prominent American engineer and physicist renowned for his pioneering contributions to photonics and optoelectronics, particularly in the development of high-speed and energy-efficient optical communication technologies.
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B.
Michael Tapper
Michael Tapper is an American drummer best known for his work in indie and experimental rock bands, including his tenure with the group Grizzly Bear.
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C.
John Kibler
John Kibler was a longtime Major League Baseball umpire best known for serving as crew chief during the 1986 World Series.
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D.
Steve Judd
Steve Judd is the aging, principled former lawman at the heart of the Western film "Ride the High Country," whose moral integrity drives the story’s central conflict.
-
E.
Michael Jeter
Michael Jeter was an American character actor known for his eccentric, heartfelt performances in film, television, and theater, including notable roles in projects like The Green Mile, Evening Shade, and Sesame Street.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: John Jumper Target entity description: John Jumper is a computational biologist best known as the lead researcher behind DeepMind’s AlphaFold, the breakthrough AI system for predicting protein structures.
-
A.
John Bowers
John Bowers is a prominent American engineer and physicist renowned for his pioneering contributions to photonics and optoelectronics, particularly in the development of high-speed and energy-efficient optical communication technologies.
-
B.
Michael Tapper
Michael Tapper is an American drummer best known for his work in indie and experimental rock bands, including his tenure with the group Grizzly Bear.
-
C.
John Kibler
John Kibler was a longtime Major League Baseball umpire best known for serving as crew chief during the 1986 World Series.
-
D.
Steve Judd
Steve Judd is the aging, principled former lawman at the heart of the Western film "Ride the High Country," whose moral integrity drives the story’s central conflict.
-
E.
Michael Jeter
Michael Jeter was an American character actor known for his eccentric, heartfelt performances in film, television, and theater, including notable roles in projects like The Green Mile, Evening Shade, and Sesame Street.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
computational biologist
ⓘ
researcher ⓘ scientist ⓘ |
| affiliation | Google DeepMind NERFINISHED ⓘ |
| areaOfInfluence |
AI for scientific discovery
ⓘ
computational structural biology community ⓘ |
| awardReceived |
Albert Lasker Basic Medical Research Award
NERFINISHED
ⓘ
BBVA Foundation Frontiers of Knowledge Award NERFINISHED ⓘ Breakthrough Prize in Life Sciences NERFINISHED ⓘ Canada Gairdner International Award NERFINISHED ⓘ Gothenburg Lise Meitner Prize NERFINISHED ⓘ Gruber Genetics Prize NERFINISHED ⓘ Pearl Meister Greengard Prize NERFINISHED ⓘ Royal Society Mullard Award NERFINISHED ⓘ Warren Alpert Foundation Prize NERFINISHED ⓘ |
| countryOfCitizenship |
United States of America
ⓘ
surface form:
United States
|
| educatedAt |
University of Chicago
ⓘ
University of Texas at Austin ⓘ Vanderbilt University NERFINISHED ⓘ |
| employer | DeepMind NERFINISHED ⓘ |
| fieldOfWork |
artificial intelligence
ⓘ
computational biology ⓘ machine learning ⓘ structural biology ⓘ |
| hasAcademicDegree |
PhD in theoretical chemistry
ⓘ
degree in physics ⓘ |
| hasCollaboratedWith |
AlphaFold research team
ⓘ
Demis Hassabis NERFINISHED ⓘ Pushmeet Kohli NERFINISHED ⓘ |
| knownFor |
applications of artificial intelligence in biology
ⓘ
leading the development of AlphaFold ⓘ protein structure prediction research ⓘ |
| languageSpoken | English ⓘ |
| notableAchievement | demonstrating near-experimental accuracy in protein structure prediction at CASP14 ⓘ |
| notablePublication |
Applying and improving AlphaFold at scale
NERFINISHED
ⓘ
Highly accurate protein structure prediction with AlphaFold NERFINISHED ⓘ |
| notableWork | AlphaFold NERFINISHED ⓘ |
| occupation |
research scientist
ⓘ
team lead at DeepMind ⓘ |
| participatedIn | CASP14 protein structure prediction competition NERFINISHED ⓘ |
| researchInterest |
biomolecular structure prediction
ⓘ
deep learning ⓘ differentiable programming ⓘ protein folding ⓘ |
| role | lead researcher on AlphaFold ⓘ |
| worksAt | 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.
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.
Subject: John Jumper Description of subject: John Jumper is a computational biologist best known as the lead researcher behind DeepMind’s AlphaFold, the breakthrough AI system for predicting protein structures.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.