Tobias Kohn
E253902
Tobias Kohn is a computer scientist and software developer known for his contributions to the Python language, including co-authoring PEP 622 on pattern matching.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Tobias Kohn canonical | 4 |
How this entity was disambiguated
This entity first appeared as the object of triple T2301348 — 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: Tobias Kohn Context triple: [PEP 622, author, Tobias Kohn]
-
A.
Tobias Fünke
Tobias Fünke is a socially awkward, aspiring actor and former analyst-therapist known for his oblivious behavior and unintentional double entendres in the television series "Arrested Development."
-
B.
Markus Morgenstern
Markus Morgenstern is a mathematician known for his contributions to combinatorics and graph theory.
-
C.
Christopher Scholz
Christopher Scholz is a prominent geophysicist renowned for his influential work on the mechanics of earthquakes and faulting.
-
D.
Michael Ohoven
Michael Ohoven is a German film producer and founder of Infinity Media, known for producing acclaimed independent films such as "Capote."
-
E.
Gerwin Klein
Gerwin Klein is a computer scientist known for his work in formal verification, particularly the seL4 microkernel verification project.
- 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: Tobias Kohn Target entity description: Tobias Kohn is a computer scientist and software developer known for his contributions to the Python language, including co-authoring PEP 622 on pattern matching.
-
A.
Tobias Fünke
Tobias Fünke is a socially awkward, aspiring actor and former analyst-therapist known for his oblivious behavior and unintentional double entendres in the television series "Arrested Development."
-
B.
Markus Morgenstern
Markus Morgenstern is a mathematician known for his contributions to combinatorics and graph theory.
-
C.
Christopher Scholz
Christopher Scholz is a prominent geophysicist renowned for his influential work on the mechanics of earthquakes and faulting.
-
D.
Michael Ohoven
Michael Ohoven is a German film producer and founder of Infinity Media, known for producing acclaimed independent films such as "Capote."
-
E.
Gerwin Klein
Gerwin Klein is a computer scientist known for his work in formal verification, particularly the seL4 microkernel verification project.
- F. None of above. chosen
Statements (24)
| Predicate | Object |
|---|---|
| instanceOf |
Python Enhancement Proposal
ⓘ
computer scientist ⓘ software developer ⓘ |
| areaOfExpertise |
Python interpreter and semantics
ⓘ
language design ⓘ pattern matching in programming languages ⓘ |
| associatedWith | Python community ⓘ |
| author | Tobias Kohn ⓘ |
| coAuthorOf | PEP 622 ⓘ |
| contributedTo | Python language design ⓘ |
| fieldOfWork |
computer science
ⓘ
programming languages ⓘ software engineering ⓘ |
| hasRole |
PEP author
ⓘ
Python language contributor ⓘ |
| knownFor |
contributions to the Python programming language
ⓘ
work on structural pattern matching in Python ⓘ |
| language | English ⓘ |
| mainSubject | structural pattern matching ⓘ |
| notableConcept | structural pattern matching in Python ⓘ |
| notableWork | PEP 622 ⓘ |
| occupation |
computer scientist
ⓘ
software developer ⓘ |
| programmingLanguage | Python ⓘ |
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: Tobias Kohn Description of subject: Tobias Kohn is a computer scientist and software developer known for his contributions to the Python language, including co-authoring PEP 622 on pattern matching.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.