Michal Zielinski
E736808
Michal Zielinski is a researcher who contributed as a co-author to the landmark 2021 Nature paper on AlphaFold by Jumper et al., which revolutionized protein structure prediction using deep learning.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Michal Zielinski canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8482657 — 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: Michal Zielinski Context triple: [Jumper et al., Nature 2021, hasAuthor, Michal Zielinski]
-
A.
Kamil Grosicki
Kamil Grosicki is a Polish professional footballer and winger known for his pace, creativity, and long international career with the Poland national team.
-
B.
Grzegorz Lato
Grzegorz Lato is a former Polish footballer renowned as one of Poland’s greatest forwards, celebrated for his prolific scoring and key role in the national team’s successes in the 1970s.
-
C.
Andrzej Gwiazda
Andrzej Gwiazda is a Polish engineer, opposition activist, and one of the key co-founders and leaders of the Solidarity (Solidarność) trade union movement against the communist regime in Poland.
-
D.
Filip Wolski
Filip Wolski is a machine learning researcher known for his work at OpenAI, including contributions to reinforcement learning methods such as Proximal Policy Optimization (PPO).
-
E.
Bartosz Zoltak
Bartosz Zoltak is a cryptographer best known for creating the VMPC stream cipher and related cryptographic constructions.
- 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: Michal Zielinski Target entity description: Michal Zielinski is a researcher who contributed as a co-author to the landmark 2021 Nature paper on AlphaFold by Jumper et al., which revolutionized protein structure prediction using deep learning.
-
A.
Kamil Grosicki
Kamil Grosicki is a Polish professional footballer and winger known for his pace, creativity, and long international career with the Poland national team.
-
B.
Grzegorz Lato
Grzegorz Lato is a former Polish footballer renowned as one of Poland’s greatest forwards, celebrated for his prolific scoring and key role in the national team’s successes in the 1970s.
-
C.
Andrzej Gwiazda
Andrzej Gwiazda is a Polish engineer, opposition activist, and one of the key co-founders and leaders of the Solidarity (Solidarność) trade union movement against the communist regime in Poland.
-
D.
Filip Wolski
Filip Wolski is a machine learning researcher known for his work at OpenAI, including contributions to reinforcement learning methods such as Proximal Policy Optimization (PPO).
-
E.
Bartosz Zoltak
Bartosz Zoltak is a cryptographer best known for creating the VMPC stream cipher and related cryptographic constructions.
- F. None of above. chosen
Statements (25)
| Predicate | Object |
|---|---|
| instanceOf |
deep learning model
ⓘ
protein structure prediction system ⓘ researcher ⓘ scientific article ⓘ scientist ⓘ |
| basedOn |
artificial intelligence
ⓘ
deep learning ⓘ |
| coAuthorOf | Highly accurate protein structure prediction with AlphaFold ⓘ |
| contributedTo | AlphaFold ⓘ |
| developer | DeepMind ⓘ |
| fieldOfWork |
computational biology
ⓘ
computational biology ⓘ deep learning ⓘ machine learning ⓘ protein structure prediction ⓘ structural biology ⓘ |
| hasRole | co-author ⓘ |
| knownFor |
contributions to protein structure prediction using deep learning
ⓘ
work on AlphaFold ⓘ |
| mainSubject |
AlphaFold
ⓘ
protein structure prediction ⓘ |
| notablePublication | Highly accurate protein structure prediction with AlphaFold ⓘ |
| notableWork | co-author of 2021 Nature AlphaFold paper by Jumper et al. ⓘ |
| publicationYear | 2021 ⓘ |
| publishedIn | Nature ⓘ |
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: Michal Zielinski Description of subject: Michal Zielinski is a researcher who contributed as a co-author to the landmark 2021 Nature paper on AlphaFold by Jumper et al., which revolutionized protein structure prediction using deep learning.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.