Triple
T34585447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sahoo |
E888030
|
entity |
| Predicate | hasTypicalOccupationAssociation |
P116875
|
FINISHED |
| Object | mercantile occupations |
—
|
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: mercantile occupations | Statement: [Sahoo, hasTypicalOccupationAssociation, mercantile occupations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalOccupationAssociation Context triple: [Sahoo, hasTypicalOccupationAssociation, mercantile occupations]
-
A.
hasTypicalOccupation
chosen
Indicates that an entity commonly or characteristically works in a particular job or profession.
-
B.
occupationalAssociation
Indicates a relationship where one entity is connected to another through a job, profession, or work-related role.
-
C.
hasOccupationAttribute
Indicates that an entity possesses a specific characteristic, quality, or attribute related to its occupation or job.
-
D.
hasOccupationInWork
Indicates that an entity holds or performs a specific occupation within a particular work, project, or creative production.
-
E.
recognizedOccupationOf
Indicates that one entity is acknowledged or officially accepted as the occupation or professional role held by another entity.
- 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_69f349d25cbc8190869998de5915886b |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff397e19a88190a945b826159f5290 |
completed | May 9, 2026, 1:41 p.m. |
| PD | Predicate disambiguation | batch_69ff392400d0819088d30d08d4a774bd |
completed | May 9, 2026, 1:39 p.m. |
Created at: May 1, 2026, 2:03 a.m.