Pamela Goynes-Brown
E442657
Pamela Goynes-Brown is an American politician who serves as the mayor of North Las Vegas, Nevada.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Pamela Goynes-Brown canonical | 1 |
Statements (14)
| Predicate | Object |
|---|---|
| instanceOf |
human
ⓘ
mayor ⓘ politician ⓘ |
| country | United States of America ⓘ |
| countryOfCitizenship | United States of America ⓘ |
| familyName | Goynes-Brown NERFINISHED ⓘ |
| givenName | Pamela NERFINISHED ⓘ |
| locatedInTheAdministrativeTerritorialEntity | Clark County, Nevada NERFINISHED ⓘ |
| occupation | politician ⓘ |
| officeHeldInJurisdiction | North Las Vegas, Nevada NERFINISHED ⓘ |
| politicalRole | mayor of North Las Vegas, Nevada ⓘ |
| positionHeld | mayor of North Las Vegas ⓘ |
| residence | North Las Vegas, Nevada NERFINISHED ⓘ |
| workLocation | North Las Vegas, Nevada NERFINISHED ⓘ |
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: Pamela Goynes-Brown Description of subject: Pamela Goynes-Brown is an American politician who serves as the mayor of North Las Vegas, Nevada.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.