Triple
T391959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kevin McHale |
E8899
|
entity |
| Predicate | careerPointsPerGame |
P10820
|
FINISHED |
| Object | 17.9 |
—
|
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: 17.9 | Statement: [Kevin McHale, careerPointsPerGame, 17.9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerPointsPerGame Context triple: [Kevin McHale, careerPointsPerGame, 17.9]
-
A.
MVPPoints
Indicates the number of points an entity has earned toward a Most Valuable Player (MVP) recognition or award.
-
B.
scoredOverPointsCareer
Indicates that an entity (typically an athlete) accumulated more than a specified number of points over the course of their entire career.
-
C.
MVPThreePointersAttempted
Indicates the number of three-point shots attempted by the MVP in a given context or period.
-
D.
careerRunsScored
Indicates the total number of runs an entity has scored over the entire duration of their playing career.
-
E.
MVPThreePointersMade
Indicates the number of three-point field goals successfully made by the MVP in a game or season.
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec7492288190bf33c9c869a0710f |
completed | Feb. 28, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69a2e96a8ca48190abbd8de9b02c115c |
completed | Feb. 28, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69a2ea2dc3088190a2aeb4496aff3582 |
completed | Feb. 28, 2026, 1:14 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.