Triple
T38518422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jalen & Jacoby |
E922401
|
entity |
| Predicate | hasPresenterBackground |
P15585
|
FINISHED |
| Object | Jalen Rose is a former NBA player |
E269602
|
NE 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: Jalen Rose is a former NBA player | Statement: [Jalen & Jacoby, hasPresenterBackground, Jalen Rose is a former NBA player]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPresenterBackground Context triple: [Jalen & Jacoby, hasPresenterBackground, Jalen Rose is a former NBA player]
-
A.
hasPresentation
Indicates that an entity delivers, contains, or is associated with a specific presentation (such as a talk, slide deck, or formal display of information).
-
B.
hasBackground
chosen
Indicates that an entity possesses or is associated with a particular background, such as context, setting, or prior circumstances.
-
C.
hasPresenterType
Indicates the specific role or category of presenter associated with an event, item, or presentation.
-
D.
hasPresenterName
Indicates that an entity serving as a presenter is associated with a specific name used to identify them.
-
E.
hasLeaderBackground
Indicates that an entity’s leader possesses a specific background, such as a particular profession, experience, or qualification.
- F. None of above.
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_69f76ea5f5588190bd0b28c82e975640 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41d665d088819094e5fae27bf825ca |
completed | June 29, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_6a037a1e32108190897356d6a7fed879 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:32 p.m.