Sophia Doyle
E824642
Sophia Doyle is a fictional character played by American actress McKaley Miller, known from her work in film and television.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Sophia Doyle canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T9801700 — 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: Sophia Doyle Context triple: [McKaley Miller, portrayed, Sophia Doyle]
-
A.
Sophia Hitchens
Sophia Hitchens is the daughter of the late British-American author and polemicist Christopher Hitchens.
-
B.
Emma Doyle
Emma Doyle is an American political aide who has served in senior White House operational and advisory roles, including as a top staffer in the Trump administration.
-
C.
Grace Delaney
Grace Delaney is a central character in the novel "Running Dog," known for her involvement in the dark, conspiratorial world surrounding a mysterious and controversial film.
-
D.
Sophia Pitt
Sophia Pitt was an 18th-century English gentlewoman known primarily as the wife of Royal Navy admiral Sir George Pocock.
-
E.
Mia Dolan
Mia Dolan is an aspiring actress in Los Angeles and one of the two central protagonists of the musical film "La La Land."
- 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: Sophia Doyle Target entity description: Sophia Doyle is a fictional character played by American actress McKaley Miller, known from her work in film and television.
-
A.
Sophia Hitchens
Sophia Hitchens is the daughter of the late British-American author and polemicist Christopher Hitchens.
-
B.
Emma Doyle
Emma Doyle is an American political aide who has served in senior White House operational and advisory roles, including as a top staffer in the Trump administration.
-
C.
Grace Delaney
Grace Delaney is a central character in the novel "Running Dog," known for her involvement in the dark, conspiratorial world surrounding a mysterious and controversial film.
-
D.
Sophia Pitt
Sophia Pitt was an 18th-century English gentlewoman known primarily as the wife of Royal Navy admiral Sir George Pocock.
-
E.
Mia Dolan
Mia Dolan is an aspiring actress in Los Angeles and one of the two central protagonists of the musical film "La La Land."
- F. None of above. chosen
Statements (7)
| Predicate | Object |
|---|---|
| instanceOf | fictional character ⓘ |
| fictionalUniverse | television series universe ⓘ |
| hasGender | female ⓘ |
| medium |
film
ⓘ
television ⓘ |
| portrayedBy | McKaley Miller ⓘ |
| portrayedByNationality | American ⓘ |
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: Sophia Doyle Description of subject: Sophia Doyle is a fictional character played by American actress McKaley Miller, known from her work in film and television.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.