Triple
T13131988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | bimaristan |
E311984
|
entity |
| Predicate | hadStaffRole |
P108233
|
FINISHED |
| Object | physician |
—
|
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: physician | Statement: [bimaristan, hadStaffRole, physician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadStaffRole Context triple: [bimaristan, hadStaffRole, physician]
-
A.
hasFormerStaffMember
Indicates that an entity once had a person as a staff member, but that person is no longer employed there.
-
B.
hasStageRole
Indicates that an entity performs or holds a specific role or character in a staged performance or theatrical production.
-
C.
hasNotableRoleIn
Indicates that an entity holds a significant or noteworthy role or function within another entity, event, work, or context.
-
D.
hasLegalRole
Indicates that an entity holds a specific legal capacity, status, or function in relation to another entity or context.
-
E.
mayHavePriorRole
Indicates that an entity is allowed or expected to have held a specified role at some earlier time.
- F. None of above. chosen
Provenance (4 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_69d806a9fe888190b081e2d9ea665d6c |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d981b27a8c81909a92ab7be5d3a7e9 |
completed | April 10, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69d98043a74c81908648e6cd0b4c7f71 |
completed | April 10, 2026, 10:57 p.m. |
| PDg | Predicate description generation | batch_69d98134df64819084a5674f9475dcc2 |
completed | April 10, 2026, 11:01 p.m. |
Created at: April 9, 2026, 9:08 p.m.