Triple
T8563190
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | René Belloq |
E202737
|
entity |
| Predicate | professionType |
P75042
|
FINISHED |
| Object | archaeological rival |
—
|
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: archaeological rival | Statement: [René Belloq, professionType, archaeological rival]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionType Context triple: [René Belloq, professionType, archaeological rival]
-
A.
occupationType
chosen
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
-
B.
careerType
Indicates the kind or category of professional occupation or career path associated with an entity.
-
C.
professionalSector
Indicates the industry or field in which an entity conducts its professional or occupational activities.
-
D.
vocationType
Indicates the specific kind or category of occupation, profession, or calling associated with an entity.
-
E.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
- 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_69ca8326e6c881908ff720d6abaebdc5 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe9cfc4a48190ae4530d3614d115f |
completed | March 31, 2026, 3:35 p.m. |
| PD | Predicate disambiguation | batch_69cbd1160fcc8190aa380a73610af731 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:20 p.m.