Triple
T12413715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Old Bedford |
E296581
|
entity |
| Predicate | subject genre |
P14
|
FINISHED |
| Object | music hall scene |
—
|
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: music hall scene | Statement: [The Old Bedford, subject genre, music hall scene]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subject genre Context triple: [The Old Bedford, subject genre, music hall scene]
-
A.
genre
chosen
Indicates the artistic or thematic category to which a work (such as a book, film, or song) belongs.
-
B.
visualGenre
Indicates the visual or stylistic category to which something belongs, such as its artistic or cinematic genre.
-
C.
genreWithin
Indicates that one genre is a subgenre or more specific category contained within another, broader genre.
-
D.
targetGenre
Indicates the genre that something is specifically aimed at, categorized under, or intended to belong to.
-
E.
secondaryGenre
Indicates that an entity (such as a work or item) has an additional, non-primary genre classification associated with it.
- 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_69d6ad9f464c81909db36d7e96e34b9e |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94e1888b48190bd750f839a26e99e |
completed | April 10, 2026, 7:23 p.m. |
| PD | Predicate disambiguation | batch_69d94d354b488190adc83fb4f2770dd5 |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:55 p.m.