Triple
T37409673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jun-fan |
E929526
|
entity |
| Predicate | hasBearerFieldOfWork |
P73996
|
FINISHED |
| Object | martial arts cinema |
—
|
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: martial arts cinema | Statement: [Jun-fan, hasBearerFieldOfWork, martial arts cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBearerFieldOfWork Context triple: [Jun-fan, hasBearerFieldOfWork, martial arts cinema]
-
A.
hasWorkField
chosen
Indicates that an entity is associated with or operates within a particular field or area of work.
-
B.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
C.
hasNotableBearerOccupation
Indicates that an entity is associated with a notable person who holds a specific occupation.
-
D.
bearerField
Indicates that one entity serves as the bearer or holder of a specific field, attribute, or property associated with another entity.
-
E.
hasSectionOfWorkType
Indicates that an entity is associated with a specific type or category of work section it involves or contains.
- 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_69f76ebde49481908566cd96b37ccc84 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd485f57dc8190820365396d041991 |
completed | May 8, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_69fd47d35da081908bec8901018d186c |
completed | May 8, 2026, 2:17 a.m. |
Created at: May 3, 2026, 4:16 p.m.