Triple
T36278766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 橋本忍 |
E892878
|
entity |
| Predicate | 関連するジャンル |
P38924
|
FINISHED |
| Object | 時代劇映画 |
—
|
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: 時代劇映画 | Statement: [橋本忍, 関連するジャンル, 時代劇映画]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 関連するジャンル Context triple: [橋本忍, 関連するジャンル, 時代劇映画]
-
A.
genreAssociatedWith
chosen
Indicates a relationship where a work, item, or entity is linked to or categorized under a particular genre.
-
B.
isAssociatedWithSubgenre
Indicates that one entity has a connection or linkage to a specific subgenre of a broader category.
-
C.
genreRelation
Indicates a relationship where one entity is categorized as having, belonging to, or being associated with a particular genre defined by another entity.
-
D.
hasGenreRelation
Indicates that there is an association between an entity and a specific genre, specifying the type or category it belongs to.
-
E.
referencedInGenre
Indicates that one entity is mentioned, cited, or otherwise referred to within the context of a particular genre.
- 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_69f76e488f34819083e254dbe288c27a |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7ba6d06f48190a71b5a2f19e2232f |
completed | May 3, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a4aad48190a62e41c5e39339d9 |
completed | May 3, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:09 p.m.