Triple
T1384449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moses Malone |
E29811
|
entity |
| Predicate | NBA_careerPoints |
P27224
|
FINISHED |
| Object | 27409 |
—
|
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: 27409 | Statement: [Moses Malone, NBA_careerPoints, 27409]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: NBA_careerPoints Context triple: [Moses Malone, NBA_careerPoints, 27409]
-
A.
careerPointsPerGame
Indicates the average number of points an individual scores per game over the course of their entire career.
-
B.
careerReboundsPerGame
Indicates the average number of rebounds a player records per game over the course of their entire career.
-
C.
scoredOverPointsCareer
Indicates that an entity (typically an athlete) accumulated more than a specified number of points over the course of their entire career.
-
D.
NBAAllTimeScoringRank
Indicates the position an NBA player holds on the all-time career points scored leaderboard relative to all other players.
-
E.
careerRunsScored
Indicates the total number of runs an entity has scored over the entire duration of their playing career.
- 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_69a498dc92f8819094a1108f8ac90f43 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c33896548190b44f70c9aaaed9b6 |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befe343c81909f758440a531b5be |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c0335f7081908d50046ced4cdee0 |
completed | March 1, 2026, 10:39 p.m. |
Created at: March 1, 2026, 7:59 p.m.