Triple
T13469422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chuck Liddell |
E311588
|
entity |
| Predicate | professionalMMARecordWins |
P8292
|
FINISHED |
| Object | 21 |
—
|
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: 21 | Statement: [Chuck Liddell, professionalMMARecordWins, 21]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalMMARecordWins Context triple: [Chuck Liddell, professionalMMARecordWins, 21]
-
A.
numberOfProfessionalFights
Indicates the total count of professional-level fights associated with an entity (such as a person or competitor).
-
B.
professionalMMAStartYear
Indicates the calendar year in which an individual began competing in professional mixed martial arts.
-
C.
professionalRecordKOs
Indicates the number of times an entity has won by knockout (KOs) in its professional record.
-
D.
careerWins
chosen
Indicates the total number of wins an individual or entity has accumulated over the course of their entire career.
-
E.
professionalRecordDraws
Indicates the number of times a professional competitor’s official matches have ended in a draw.
- 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_69d806a938b8819097ec43a2229fc7f9 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf21e46081908a00c9acf54f270f |
completed | April 12, 2026, 2:41 p.m. |
| PD | Predicate disambiguation | batch_69dbadfddefc81909ef7fde23b181b5c |
completed | April 12, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:42 p.m.