Triple
T3536192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dale Arbus |
E74777
|
entity |
| Predicate | employerOccupation |
P44925
|
FINISHED |
| Object | dentist |
—
|
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: dentist | Statement: [Dale Arbus, employerOccupation, dentist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerOccupation Context triple: [Dale Arbus, employerOccupation, dentist]
-
A.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
B.
recipientOccupation
Indicates that the object specifies the job, profession, or role held by the recipient in the described relationship or event.
-
C.
employedRole
chosen
Indicates that an entity holds or performs a specific role or position within an employment or work context.
-
D.
workPosition
Indicates the specific job role or position that an entity holds within an organization or workplace.
-
E.
employmentType
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
- 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_69ad85d1a3948190931fd1ea1f49717b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbcc7b92481908d2d99948780f4d0 |
completed | March 8, 2026, 6:15 p.m. |
| PD | Predicate disambiguation | batch_69adae13ab808190a5d6ecdc7543445e |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:20 p.m.