Triple
T36347522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ziyi |
E895105
|
entity |
| Predicate | associatedIndustryOfNotableBearer |
P13800
|
FINISHED |
| Object | film industry |
—
|
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: film industry | Statement: [Ziyi, associatedIndustryOfNotableBearer, film industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedIndustryOfNotableBearer Context triple: [Ziyi, associatedIndustryOfNotableBearer, film industry]
-
A.
notableIndustry
chosen
Indicates that an entity is significantly recognized or prominent within a specified industry or sector.
-
B.
associatedCompanyNotableRole
Indicates that an entity has a notable role or position in a specified associated company.
-
C.
notablyAssociatedWith
Indicates that one entity is prominently or distinctively connected with another in a way that is especially noteworthy or remarkable.
-
D.
employerOfNotablePerson
Indicates that an entity serves or has served as the employer of a person who is considered notable.
-
E.
associatedCompanyKnownFor
Indicates that a company linked to an entity is recognized or notable for a particular product, service, activity, or characteristic.
- 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_69f76e4f437c8190a1af3ea2564f41f5 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0a54cc8190868c1bfa1590d1a6 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:09 p.m.