Triple
T14507957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Becca and Tyler |
E340314
|
entity |
| Predicate | associatedWithGenreElement |
P114508
|
FINISHED |
| Object | twist ending |
—
|
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: twist ending | Statement: [Becca and Tyler, associatedWithGenreElement, twist ending]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithGenreElement Context triple: [Becca and Tyler, associatedWithGenreElement, twist ending]
-
A.
associatedWithGenreScene
Indicates that an entity is connected or related to a particular genre scene, such as a specific stylistic or cultural subcommunity within a broader genre.
-
B.
genreAssociatedWith
Indicates a relationship where a work, item, or entity is linked to or categorized under a particular genre.
-
C.
belongsToWorkGenre
Indicates that a creative work is classified under or associated with a particular genre.
-
D.
associatedWithGenreDevelopment
Indicates a relationship where something has contributed to, influenced, or been involved in the development or evolution of a particular genre.
-
E.
hasGenreArtist
Indicates that an artist is associated with or specializes in a particular genre.
- 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_69d822d9c0408190b9a2b3643e58bb4d |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69de94e40e44819084f323f8f9982b75 |
completed | April 14, 2026, 7:26 p.m. |
| PD | Predicate disambiguation | batch_69de5c4ccba08190a988bfda0bc9f5cb |
completed | April 14, 2026, 3:25 p.m. |
| PDg | Predicate description generation | batch_69de5fb4de14819092acdecbd201d672 |
completed | April 14, 2026, 3:39 p.m. |
Created at: April 10, 2026, 1:21 a.m.