Triple
T26298595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pretty Boy |
E661485
|
entity |
| Predicate | bearerProfessionalRecord |
P71302
|
FINISHED |
| Object | 50–0 (27 KOs) |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 50–0 (27 KOs) | Statement: [Pretty Boy, bearerProfessionalRecord, 50–0 (27 KOs)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bearerProfessionalRecord Context triple: [Pretty Boy, bearerProfessionalRecord, 50–0 (27 KOs)]
-
A.
professionalBase
Indicates that one entity serves as the primary professional location, organization, or base of operations for another entity.
-
B.
hasProfessionalReputationFor
Indicates that an entity is recognized by others as being notably associated with a particular professional quality, skill, or behavior.
-
C.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
D.
professionalWins
chosen
Indicates that one entity has achieved a certain number of victories or successes in a professional context, such as in a career, competition, or formal domain.
-
E.
professionAttribute
Indicates that a specific attribute, quality, or characteristic is associated with a given profession.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ee812cd48c81908054068f545f0526 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f650c70d7c819093d9a0f005f7c8d5 |
completed | May 2, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69f64cab1f648190a2a9460690d18a37 |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 26, 2026, 10:14 p.m.