Triple
T32726136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 蕲春李时珍故里 |
E836806
|
entity |
| Predicate | 相关人物职业 |
P2374
|
FINISHED |
| Object | 医药学家 |
—
|
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: 医药学家 | Statement: [蕲春李时珍故里, 相关人物职业, 医药学家]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 相关人物职业 Context triple: [蕲春李时珍故里, 相关人物职业, 医药学家]
-
A.
namedPersonOccupation
Indicates that a person is explicitly identified as having a particular occupation or job role.
-
B.
associatedWithCareerOf
Indicates a relationship where something is connected or relevant to a person’s professional life, occupation, or career trajectory.
-
C.
occupationAsPersona
Indicates that an entity holds or performs a particular occupation specifically in the role or persona of another characterized identity.
-
D.
subjectOccupation
chosen
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
E.
followsCharacterOccupation
Indicates that one character’s occupation or job role comes after or succeeds another character’s occupation in a sequence or progression.
- 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_69f34935455881909088975d79460418 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6c8b80b508190b03c5a5859c695fe |
completed | May 3, 2026, 4:02 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f617c08190a70ba880210f908c |
completed | May 3, 2026, 3:41 a.m. |
Created at: May 1, 2026, 1:11 a.m.