Triple
T5849381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Devin Booker |
E129989
|
entity |
| Predicate | scored70PointsInSingleGame |
P66708
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Devin Booker, scored70PointsInSingleGame, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scored70PointsInSingleGame Context triple: [Devin Booker, scored70PointsInSingleGame, true]
-
A.
scored71PointsInSingleGame
Indicates that an entity achieved a total of 71 points in a single game.
-
B.
scored100PointsInAGame
Indicates that an entity achieved a total of 100 points in a single game or match.
-
C.
championshipGameScore
Indicates the final score achieved by each participant in a championship 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.
teamDuring73PointGame
Indicates that the entity was the team involved during the referenced 73-point game event.
- 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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03c9239e08190bff7ef2bd6d21ae0 |
completed | March 22, 2026, 7:01 p.m. |
| PD | Predicate disambiguation | batch_69c0334412388190bc594794ec5754f9 |
completed | March 22, 2026, 6:21 p.m. |
| PDg | Predicate description generation | batch_69c03c8d579081909d7b97fc9014b5d7 |
completed | March 22, 2026, 7:01 p.m. |
Created at: March 22, 2026, 3:55 p.m.