Triple
T28591305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tara Knowles |
E723651
|
entity |
| Predicate | hasProfessionRole |
P124115
|
FINISHED |
| Object | hospital attending 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: hospital attending physician | Statement: [Tara Knowles, hasProfessionRole, hospital attending physician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionRole Context triple: [Tara Knowles, hasProfessionRole, hospital attending physician]
-
A.
hasGivenProfession
chosen
Indicates that an entity holds or practices a specified profession or occupation.
-
B.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
C.
hasProfessionTrait
Indicates that an entity possesses a particular characteristic, quality, or attribute specifically related to their profession or occupational role.
-
D.
hasProfessionInNarrative
Indicates that an entity holds or is assigned a particular profession or occupational role within the context of a narrative or story.
-
E.
hasIndustryRole
Indicates that an entity holds or performs a specific role, function, or position within a particular industry or sector.
- 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_69f01d7f92e481909847f5f3f3174a89 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69fef5cf8da881908260ec633830375d |
completed | May 9, 2026, 8:52 a.m. |
| PD | Predicate disambiguation | batch_69fef455e40481909861c82007b79bc0 |
completed | May 9, 2026, 8:46 a.m. |
Created at: April 28, 2026, 4:20 a.m.