Triple
T27667703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dion Waiters |
E697270
|
entity |
| Predicate | playedHighSchoolBasketballIn |
P173700
|
FINISHED |
| Object | Philadelphia, Pennsylvania |
—
|
NE NERFINISHED |
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: Philadelphia, Pennsylvania | Statement: [Dion Waiters, playedHighSchoolBasketballIn, Philadelphia, Pennsylvania]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: playedHighSchoolBasketballIn Context triple: [Dion Waiters, playedHighSchoolBasketballIn, Philadelphia, Pennsylvania]
-
A.
playedHighSchoolBasketballAt
Indicates that a person was a member of and participated on the basketball team of a particular high school.
-
B.
playedHighSchoolBasketballWith
Indicates that two people were teammates on the same high school basketball team.
-
C.
playedCollegeBasketballFor
Indicates that a person was a member of and competed for a specific college or university’s basketball team.
-
D.
playedBasketballFor
Indicates that one entity was a member of and competed for another entity’s basketball team.
-
E.
hasBasketballLevel
Indicates that an entity possesses a specified level of skill, proficiency, or ranking in basketball.
- 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_69ef590b85a4819083ec7c12bd3c9c10 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f6b9a84ff88190ab5a71f7ef1e0dac |
completed | May 3, 2026, 2:57 a.m. |
| PD | Predicate disambiguation | batch_69f6b626120c819097c9ad04487570d7 |
completed | May 3, 2026, 2:42 a.m. |
| PDg | Predicate description generation | batch_69f6b8fe147881908ba17483c7b13f05 |
completed | May 3, 2026, 2:54 a.m. |
Created at: April 27, 2026, 2:39 p.m.