Triple
T24690128
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rip Hamilton |
E611411
|
entity |
| Predicate | scoredCareerHighPointsInGame |
P145670
|
FINISHED |
| Object | 51 |
—
|
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: 51 | Statement: [Rip Hamilton, scoredCareerHighPointsInGame, 51]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scoredCareerHighPointsInGame Context triple: [Rip Hamilton, scoredCareerHighPointsInGame, 51]
-
A.
scoredCareerHighPointsInSingleGame
chosen
Indicates that an entity achieved their highest-ever number of points in a single game during the referenced event or game.
-
B.
scored70PointsInSingleGame
Indicates that an entity achieved a total of 70 points in a single game or match.
-
C.
scored71PointsInSingleGame
Indicates that an entity achieved a total of 71 points in a single game.
-
D.
scoredOverPointsCareer
Indicates that an entity (typically an athlete) accumulated more than a specified number of points over the course of their entire career.
-
E.
championshipGameScore
Indicates the final score achieved by each participant in a championship game.
- 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_69e2c4d678b081908910f4271627a31a |
completed | April 17, 2026, 11:40 p.m. |
| NER | Named-entity recognition | batch_69f410fe3b848190ae296a29f742ee30 |
completed | May 1, 2026, 2:33 a.m. |
| PD | Predicate disambiguation | batch_69f40ee8ada8819089a7016b50308ff0 |
completed | May 1, 2026, 2:24 a.m. |
Created at: April 18, 2026, 3:20 a.m.