Peter Ramsey
E378756
Peter Ramsey is an American film director best known for co-directing the acclaimed animated feature "Spider-Man: Into the Spider-Verse."
All labels observed (1)
| Label | Occurrences |
|---|---|
| Peter Ramsey canonical | 6 |
How this entity was disambiguated
This entity first appeared as the object of triple T3664810 — 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: Peter Ramsey Context triple: [Spider-Man: Into the Spider-Verse, director, Peter Ramsey]
-
A.
Matthew Robbins
Matthew Robbins is an American screenwriter and filmmaker known for his work on genre films such as Crimson Peak, Dragonslayer, and collaborations with directors like Guillermo del Toro.
-
B.
Peter Driscoll
Peter Driscoll was a South African-born British thriller novelist best known for his politically charged suspense novels set in Africa.
-
C.
Matthew Aldrich
Matthew Aldrich is an American screenwriter best known for co-writing Pixar’s Academy Award–winning animated film "Coco."
-
D.
Douglas Abrams
Douglas Abrams is an American author, editor, and literary agent best known for co-authoring spiritually focused, conversational books with prominent religious leaders such as the Dalai Lama and Desmond Tutu.
-
E.
Edward Reeves
Edward Reeves was a teacher and mentor known for instructing Lionel Logue, the Australian speech therapist famous for treating King George VI.
- 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: Peter Ramsey Target entity description: Peter Ramsey is an American film director best known for co-directing the acclaimed animated feature "Spider-Man: Into the Spider-Verse."
-
A.
Matthew Robbins
Matthew Robbins is an American screenwriter and filmmaker known for his work on genre films such as Crimson Peak, Dragonslayer, and collaborations with directors like Guillermo del Toro.
-
B.
Peter Driscoll
Peter Driscoll was a South African-born British thriller novelist best known for his politically charged suspense novels set in Africa.
-
C.
Matthew Aldrich
Matthew Aldrich is an American screenwriter best known for co-writing Pixar’s Academy Award–winning animated film "Coco."
-
D.
Douglas Abrams
Douglas Abrams is an American author, editor, and literary agent best known for co-authoring spiritually focused, conversational books with prominent religious leaders such as the Dalai Lama and Desmond Tutu.
-
E.
Edward Reeves
Edward Reeves was a teacher and mentor known for instructing Lionel Logue, the Australian speech therapist famous for treating King George VI.
- F. None of above. chosen
Statements (40)
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: Peter Ramsey Description of subject: Peter Ramsey is an American film director best known for co-directing the acclaimed animated feature "Spider-Man: Into the Spider-Verse."
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.