Triple
T15038081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shareef Abdur-Rahim |
E378527
|
entity |
| Predicate | averageReboundsPerGameInNBA |
P10821
|
FINISHED |
| Object | 7.5 |
—
|
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: 7.5 | Statement: [Shareef Abdur-Rahim, averageReboundsPerGameInNBA, 7.5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageReboundsPerGameInNBA Context triple: [Shareef Abdur-Rahim, averageReboundsPerGameInNBA, 7.5]
-
A.
careerReboundsPerGame
chosen
Indicates the average number of rebounds a player records per game over the course of their entire career.
-
B.
statRebounds
Indicates the number of rebounds an entity (typically a player or team) records in a game or over a specified period.
-
C.
ABACareerReboundsLeader
Indicates that the subject holds the record for the most total rebounds accumulated over their entire career in ABA competition.
-
D.
pointsPerGame
Indicates the average number of points an entity scores per game over a given set of games.
-
E.
franchiseReboundsLeader
Indicates the player who holds the record for the most rebounds in a franchise’s history.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded82cf3848190b0b2b6c9e65bc70b |
completed | April 15, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69de9a69d7848190b2b4662dd30f20e9 |
completed | April 14, 2026, 7:50 p.m. |
Created at: April 10, 2026, 2:59 a.m.