Triple
T32075736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ryo Kase |
E819140
|
entity |
| Predicate | hasFilmographyIn |
P197906
|
FINISHED |
| Object | film |
—
|
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 | Statement: [Ryo Kase, hasFilmographyIn, film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmographyIn Context triple: [Ryo Kase, hasFilmographyIn, film]
-
A.
hasFilmographyType
Indicates the type or category of film-related work associated with an entity (e.g., actor, director, producer) within its filmography.
-
B.
hasFilmographySection
Indicates that an entity includes a dedicated section listing films or screen-related works associated with it.
-
C.
hasWorkedOnFilmBy
Indicates that one entity has worked on a film that was created, directed, or otherwise authored by another entity.
-
D.
featuredInFilmBy
Indicates that an entity is prominently included or showcased within a film that is created, directed, or produced by a specified person or organization.
-
E.
hasFilmCareer
Indicates that an entity has been professionally involved in the film industry as a career.
- F. None of above. chosen
Provenance (4 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_69f348ff8ef88190931c08ba530a36bc |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69feb8e856d48190aa34ad8ee8376e1c |
completed | May 9, 2026, 4:32 a.m. |
| PD | Predicate disambiguation | batch_69feb82a2b6c8190a473cc25976897be |
completed | May 9, 2026, 4:29 a.m. |
| PDg | Predicate description generation | batch_69feb8e6ed7881908446745c89c30784 |
completed | May 9, 2026, 4:32 a.m. |
Created at: May 1, 2026, 12:23 a.m.