Triple
T38548239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greg Harris |
E925030
|
entity |
| Predicate | fictionalProfessionField |
P114856
|
FINISHED |
| Object | medicine |
—
|
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: medicine | Statement: [Greg Harris, fictionalProfessionField, medicine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalProfessionField Context triple: [Greg Harris, fictionalProfessionField, medicine]
-
A.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
B.
fictionalProfessionSpecialty
chosen
Indicates that a fictional character’s professional role is specialized in a particular subfield, focus area, or niche within that profession.
-
C.
fictionalProfessionContext
Indicates that an entity’s profession is defined or understood within a fictional, narrative, or imaginative context rather than as a real-world occupation.
-
D.
fictionalProfessionStatus
Indicates that an entity holds, has held, or is described as holding a profession or occupational role that is fictional rather than real.
-
E.
fictionalField
Indicates that the subject is associated with a fictional or imaginary field, domain, or area rather than a real-world one.
- 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_69f76eaeb69c8190b367df9330d6f6af |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ff7dcedab08190a719a707d03306e2 |
completed | May 9, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69ff7d0119348190ad462554e81190fe |
completed | May 9, 2026, 6:29 p.m. |
Created at: May 3, 2026, 4:32 p.m.