Genevieve Angelson
E475760
Genevieve Angelson is an American actress known for her television roles in series such as "Good Girls Revolt," "House of Lies," and "Flack."
All labels observed (1)
| Label | Occurrences |
|---|---|
| Genevieve Angelson canonical | 2 |
Statements (19)
| Predicate | Object |
|---|---|
| instanceOf |
actress
ⓘ
film actor ⓘ human ⓘ television actor ⓘ |
| countryOfCitizenship | United States of America ⓘ |
| familyName | Angelson NERFINISHED ⓘ |
| givenName | Genevieve NERFINISHED ⓘ |
| languageOfWorkOrName | English ⓘ |
| name | Genevieve Angelson NERFINISHED ⓘ |
| notableWork |
Backstrom
NERFINISHED
ⓘ
Flack NERFINISHED ⓘ Good Girls Revolt NERFINISHED ⓘ House of Lies NERFINISHED ⓘ The Afterparty NERFINISHED ⓘ The Handmaid's Tale NERFINISHED ⓘ This Is Us NERFINISHED ⓘ Titans NERFINISHED ⓘ |
| occupation | actress ⓘ |
| sexOrGender | female ⓘ |
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: Genevieve Angelson Description of subject: Genevieve Angelson is an American actress known for her television roles in series such as "Good Girls Revolt," "House of Lies," and "Flack."
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.