Triple
T3539600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guillermo Rigondeaux |
E74850
|
entity |
| Predicate | amateurCareerRecord |
P48766
|
FINISHED |
| Object | very successful Cuban national team boxer |
—
|
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: very successful Cuban national team boxer | Statement: [Guillermo Rigondeaux, amateurCareerRecord, very successful Cuban national team boxer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: amateurCareerRecord Context triple: [Guillermo Rigondeaux, amateurCareerRecord, very successful Cuban national team boxer]
-
A.
professionalRecordDraws
Indicates the number of times a professional competitor’s official matches have ended in a draw.
-
B.
careerWinLossRecord
Indicates the overall tally of wins and losses an entity has accumulated over the entire span of its career.
-
C.
isAmateur
Indicates that an entity engages in an activity or field on a non-professional, typically unpaid or hobbyist basis.
-
D.
professionalRecordNoContests
Indicates that an entity’s professional record shows no contests (e.g., bouts or matches that ended without an official result) among its outcomes.
-
E.
careerWins
Indicates the total number of wins an individual or entity has accumulated over the course of their entire career.
- F. None of above. chosen
Provenance (4 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_69ad85d274cc8190ab59c97298a1cfbf |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbccbbb5c8190a951754dda5fc642 |
completed | March 8, 2026, 6:15 p.m. |
| PD | Predicate disambiguation | batch_69adae15749881909b847c6ca73c934e |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adb0a11a1c8190baa8c0eb87ad259a |
completed | March 8, 2026, 5:23 p.m. |
Created at: March 8, 2026, 3:20 p.m.