Triple
T12171093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shelden Williams |
E289965
|
entity |
| Predicate | collegeTeamReboundsRecord |
P103134
|
FINISHED |
| Object | over 1,000 career rebounds at Duke |
—
|
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: over 1,000 career rebounds at Duke | Statement: [Shelden Williams, collegeTeamReboundsRecord, over 1,000 career rebounds at Duke]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: collegeTeamReboundsRecord Context triple: [Shelden Williams, collegeTeamReboundsRecord, over 1,000 career rebounds at Duke]
-
A.
collegeTeamPointsRecordHolder
Indicates that the subject is the record-holding individual or team for the highest number of points scored for a particular college team.
-
B.
NBAReboundingLeaderYears
Indicates the years in which an entity was the NBA leader in rebounds.
-
C.
reboundsLeaderTeam
Indicates that a team is the leading team in total rebounds in a given game, season, or competition context.
-
D.
franchiseReboundsLeader
Indicates the player who holds the record for the most rebounds in a franchise’s history.
-
E.
careerReboundsPerGame
Indicates the average number of rebounds a player records per game over the course of their entire 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_69d6ab4d6c00819095a9a7c35de83cfb |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91621ca6c81908365732f361aef13 |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150e85348190b9b47cda4a17dcd0 |
completed | April 10, 2026, 3:19 p.m. |
| PDg | Predicate description generation | batch_69d916165c708190bf0745e125589f46 |
completed | April 10, 2026, 3:24 p.m. |
Created at: April 8, 2026, 9:50 p.m.