Triple
T6025585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marc Eversley |
E134169
|
entity |
| Predicate | typeOfProfessional |
P35550
|
FINISHED |
| Object | sports executive |
—
|
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: sports executive | Statement: [Marc Eversley, typeOfProfessional, sports executive]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfProfessional Context triple: [Marc Eversley, typeOfProfessional, sports executive]
-
A.
careerType
Indicates the kind or category of professional occupation or career path associated with an entity.
-
B.
memberProfession
chosen
Indicates that a member or individual holds or practices a particular profession or occupation.
-
C.
professionServed
Indicates that an entity has performed work or provided services in a particular profession or occupational role.
-
D.
vocationType
Indicates the specific kind or category of occupation, profession, or calling associated with an entity.
-
E.
recognizesProfession
Indicates that one entity acknowledges or identifies another entity’s professional role or occupation as such.
- 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_69c008742a5c8190b9cb9c2787a3d8b3 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04fc08c5c819085afab5b18bce4d1 |
completed | March 22, 2026, 8:23 p.m. |
| PD | Predicate disambiguation | batch_69c049e75b3881908be106fbcf8c68d4 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:07 p.m.