Patricia Wettig
E410253
Patricia Wettig is an American actress best known for her Emmy-winning role on the television series "thirtysomething" and her work in film and TV dramas.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Patricia Wettig canonical | 6 |
How this entity was disambiguated
This entity first appeared as the object of triple T3760169 — 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.
Target entity: Patricia Wettig Context triple: [City Slickers, starring, Patricia Wettig]
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A.
Lindsay Crouse
Lindsay Crouse is an American actress known for her work in film, television, and theater, including an Academy Award–nominated role in "Places in the Heart."
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B.
Mercedes Ruehl
Mercedes Ruehl is an American actress acclaimed for her powerful stage and screen performances, including an Academy Award-winning role in the film "The Fisher King."
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C.
Tamara Tunie
Tamara Tunie is an American actress and director best known for her long-running role as medical examiner Melinda Warner on the television series "Law & Order: Special Victims Unit."
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D.
Glenne Headly
Glenne Headly was an American actress known for her versatile film, television, and stage performances, including prominent roles in comedies and dramas from the 1980s onward.
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E.
Amy Brenneman
Amy Brenneman is an American actress and producer best known for her roles in television series such as "NYPD Blue," "Judging Amy," and "The Leftovers."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: Patricia Wettig Target entity description: Patricia Wettig is an American actress best known for her Emmy-winning role on the television series "thirtysomething" and her work in film and TV dramas.
-
A.
Lindsay Crouse
Lindsay Crouse is an American actress known for her work in film, television, and theater, including an Academy Award–nominated role in "Places in the Heart."
-
B.
Mercedes Ruehl
Mercedes Ruehl is an American actress acclaimed for her powerful stage and screen performances, including an Academy Award-winning role in the film "The Fisher King."
-
C.
Tamara Tunie
Tamara Tunie is an American actress and director best known for her long-running role as medical examiner Melinda Warner on the television series "Law & Order: Special Victims Unit."
-
D.
Glenne Headly
Glenne Headly was an American actress known for her versatile film, television, and stage performances, including prominent roles in comedies and dramas from the 1980s onward.
-
E.
Amy Brenneman
Amy Brenneman is an American actress and producer best known for her roles in television series such as "NYPD Blue," "Judging Amy," and "The Leftovers."
- F. None of above. chosen
Statements (49)
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.
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.
Subject: Patricia Wettig Description of subject: Patricia Wettig is an American actress best known for her Emmy-winning role on the television series "thirtysomething" and her work in film and TV dramas.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.