Triple
T38550500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brian Earl Spilner |
E925096
|
entity |
| Predicate | coverStoryOccupation |
P2374
|
FINISHED |
| Object | aftermarket parts buyer |
—
|
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: aftermarket parts buyer | Statement: [Brian Earl Spilner, coverStoryOccupation, aftermarket parts buyer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coverStoryOccupation Context triple: [Brian Earl Spilner, coverStoryOccupation, aftermarket parts buyer]
-
A.
coversOccupation
Indicates that one entity provides information about, includes, or pertains to another entity’s occupation or professional role.
-
B.
authorOccupation
Indicates the professional role or job that an author holds or is associated with.
-
C.
subjectOccupation
chosen
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
D.
representedOccupation
Indicates that one entity has served as an official or formal representative of another entity’s occupation or professional role.
-
E.
occupationAsPersona
Indicates that an entity holds or performs a particular occupation specifically in the role or persona of another characterized identity.
- 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_69f76eaeb69c8190b367df9330d6f6af |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd7b0503a08190ba07338365b6fcc9 |
completed | May 8, 2026, 5:56 a.m. |
| PD | Predicate disambiguation | batch_69fd7a9733dc81909199f453c0cc2bc1 |
completed | May 8, 2026, 5:54 a.m. |
Created at: May 3, 2026, 4:32 p.m.