Triple
T7117373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sugar Ray Robinson |
E165852
|
entity |
| Predicate | professionalRecordWinsByKO |
P41311
|
FINISHED |
| Object | 108 |
—
|
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: 108 | Statement: [Sugar Ray Robinson, professionalRecordWinsByKO, 108]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalRecordWinsByKO Context triple: [Sugar Ray Robinson, professionalRecordWinsByKO, 108]
-
A.
professionalRecordKOs
chosen
Indicates the number of times an entity has won by knockout (KOs) in its professional record.
-
B.
winsByKO
Indicates that one competitor defeats another by knocking them out, ending the contest immediately.
-
C.
numberOfKnockouts
Indicates the total count of times an entity has defeated opponents by knockout.
-
D.
careerWins
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_69c6888227bc8190a1394679e3116f90 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e618bdac8190be291468b7d977bb |
completed | March 27, 2026, 8:18 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c4f9788190830288d00cc37026 |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:43 p.m.