Triple
T28707360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Thacker |
E729729
|
entity |
| Predicate | genreContextWork |
P20075
|
FINISHED |
| Object | British romantic comedy 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: British romantic comedy film | Statement: [William Thacker, genreContextWork, British romantic comedy film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genreContextWork Context triple: [William Thacker, genreContextWork, British romantic comedy film]
-
A.
genreContext
chosen
Indicates the contextual genre or categorical style associated with an entity, such as the thematic or stylistic framework in which it is situated.
-
B.
commonGenreContext
Indicates that two or more entities share a similar or related genre context, such as belonging to the same or closely related genres.
-
C.
partOfWorkGenreContext
Indicates that something occurs within or is associated with the genre-related context of a particular work.
-
D.
culturalContextOfWork
Indicates the cultural setting, traditions, or background within which a work was created, interpreted, or is meaningfully situated.
-
E.
genreContribution
Indicates that an entity has contributed to, influenced, or helped shape 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_69f043e7d5a4819094b18aca10b1e024 |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69f676f968d08190a4adba0439b438c9 |
completed | May 2, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69f675ff62c48190a634bbb8896973b9 |
completed | May 2, 2026, 10:09 p.m. |
Created at: April 28, 2026, 5:46 a.m.