Corn Griffin
E579890
Corn Griffin was an American heavyweight boxer of the 1930s, best remembered as an early comeback opponent of future world champion James J. Braddock.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Corn Griffin canonical | 1 |
Statements (16)
| Predicate | Object |
|---|---|
| instanceOf |
human
ⓘ
professional boxer ⓘ |
| activeInDecade | 1930s ⓘ |
| countryOfCitizenship | United States of America ⓘ |
| fought | James J. Braddock NERFINISHED ⓘ |
| genre | heavyweight boxing ⓘ |
| hasFamilyName | Griffin NERFINISHED ⓘ |
| hasGivenName | Corn ⓘ |
| nationality | American ⓘ |
| notableFor | being an early comeback opponent of James J. Braddock ⓘ |
| occupation | boxer ⓘ |
| opponent | James J. Braddock NERFINISHED ⓘ |
| participantIn | professional boxing ⓘ |
| sexOrGender | male ⓘ |
| sport | boxing ⓘ |
| weightClass | heavyweight ⓘ |
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: Corn Griffin Description of subject: Corn Griffin was an American heavyweight boxer of the 1930s, best remembered as an early comeback opponent of future world champion James J. Braddock.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.