Shane Hurlbut
E138142
Shane Hurlbut is an American cinematographer known for his work on major Hollywood films and his influential role in advancing digital cinematography techniques.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Shane Hurlbut canonical | 24 |
How this entity was disambiguated
This entity first appeared as the object of triple T985061 — 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: Shane Hurlbut Context triple: [Terminator Salvation, cinematographyBy, Shane Hurlbut]
-
A.
Michael Kube-McDowell
Michael Kube-McDowell is an American science fiction author known for his novels, short stories, and contributions to major franchises such as Star Wars.
-
B.
Ben York Jones
Ben York Jones is an American screenwriter and actor best known for co-writing the acclaimed romantic drama film "Like Crazy."
-
C.
Scott Reed
Scott Reed is a computer scientist and machine learning researcher known for his work on deep learning and generative models.
-
D.
Tim Laudner
Tim Laudner is a former Major League Baseball catcher best known for his years with the Minnesota Twins, including their 1987 World Series championship team.
-
E.
Jonathan Tucker
Jonathan Tucker is an American actor known for his intense, character-driven roles in film and television, including prominent performances in series like "Kingdom," "Westworld," and "City on a Hill."
- 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: Shane Hurlbut Target entity description: Shane Hurlbut is an American cinematographer known for his work on major Hollywood films and his influential role in advancing digital cinematography techniques.
-
A.
Michael Kube-McDowell
Michael Kube-McDowell is an American science fiction author known for his novels, short stories, and contributions to major franchises such as Star Wars.
-
B.
Ben York Jones
Ben York Jones is an American screenwriter and actor best known for co-writing the acclaimed romantic drama film "Like Crazy."
-
C.
Scott Reed
Scott Reed is a computer scientist and machine learning researcher known for his work on deep learning and generative models.
-
D.
Tim Laudner
Tim Laudner is a former Major League Baseball catcher best known for his years with the Minnesota Twins, including their 1987 World Series championship team.
-
E.
Jonathan Tucker
Jonathan Tucker is an American actor known for his intense, character-driven roles in film and television, including prominent performances in series like "Kingdom," "Westworld," and "City on a Hill."
- F. None of above. chosen
Statements (48)
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: Shane Hurlbut Description of subject: Shane Hurlbut is an American cinematographer known for his work on major Hollywood films and his influential role in advancing digital cinematography techniques.
Referenced by (24)
Full triples — surface form annotated when it differs from this entity's canonical label.