Triple
T5398303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lydia (Four Weddings and a Funeral) |
E120710
|
entity |
| Predicate | basedInWorkGenre |
P62560
|
FINISHED |
| Object | British romantic comedy |
—
|
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 | Statement: [Lydia (Four Weddings and a Funeral), basedInWorkGenre, British romantic comedy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedInWorkGenre Context triple: [Lydia (Four Weddings and a Funeral), basedInWorkGenre, British romantic comedy]
-
A.
basedOnWorkGenre
Indicates that one entity’s genre classification is derived from or determined by the genre of another work.
-
B.
belongsToWorkGenre
chosen
Indicates that a creative work is classified under or associated with a particular genre.
-
C.
workedOnGenre
Indicates that an entity (such as a person or organization) has done work related to a particular genre.
-
D.
genreOfOriginWork
Indicates that a work is classified under a particular genre based on the genre of its original source work.
-
E.
notableWorkGenre
Indicates that a particular work is recognized as notable for an entity and specifies the genre to which that work belongs.
- 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_69bd4637b92c8190b815b6443ae4b323 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd8932b8bc8190bd31e11b167a7212 |
completed | March 20, 2026, 5:51 p.m. |
| PD | Predicate disambiguation | batch_69bd84660ea08190a641084814fcf94d |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:04 p.m.