Triple
T8872865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kerry Kittles |
E211203
|
entity |
| Predicate | careerPointsNBA |
P27224
|
FINISHED |
| Object | over 7,000 |
—
|
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 7,000 | Statement: [Kerry Kittles, careerPointsNBA, over 7,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerPointsNBA Context triple: [Kerry Kittles, careerPointsNBA, over 7,000]
-
A.
careerPointsABA
Indicates the total number of points a player has scored over their career in the ABA (American Basketball Association).
-
B.
totalCareerPointsNBA
Indicates the total number of points a player has scored over their entire NBA career.
-
C.
NBA_careerPoints
chosen
Indicates the total number of points a player has scored over the course of their NBA career.
-
D.
careerPointsPerGame
Indicates the average number of points an individual scores per game over the course of their entire career.
-
E.
careerPoints
Indicates the total number of points an individual has accumulated over the course of their entire career in a given activity or domain.
- 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_69ca838e78748190934d82db3104f855 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc612952348190856d6964122c3f01 |
completed | April 1, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69cc5c2956788190a311c647b4da17a6 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:52 p.m.