Triple
T5968200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donnie Freeman |
E132804
|
entity |
| Predicate | careerPointsABA |
P66805
|
FINISHED |
| Object | over 11,000 |
—
|
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 11,000 | Statement: [Donnie Freeman, careerPointsABA, over 11,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerPointsABA Context triple: [Donnie Freeman, careerPointsABA, over 11,000]
-
A.
totalCareerPointsNBA
Indicates the total number of points a player has scored over their entire NBA career.
-
B.
careerPointsPerGame
Indicates the average number of points an individual scores per game over the course of their entire career.
-
C.
NBA_careerPoints
Indicates the total number of points a player has scored over the course of their NBA career.
-
D.
NBAcareerEnd
Indicates the time or season when a person's playing career in the NBA came to an end.
-
E.
careerAssistsPerGame
Indicates the average number of assists 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_69c0086deab081908550159ca23eec9b |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c03e174c7c8190b04113f6529f5f0e |
completed | March 22, 2026, 7:08 p.m. |
| PD | Predicate disambiguation | batch_69c0335b55a48190927a5b1529edb542 |
completed | March 22, 2026, 6:22 p.m. |
| PDg | Predicate description generation | batch_69c03e16476481908361fbb3049664e9 |
completed | March 22, 2026, 7:08 p.m. |
Created at: March 22, 2026, 4:03 p.m.