Triple
T24204333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York Goes to Hollywood |
E600061
|
entity |
| Predicate | partOfGenreTrend |
P137793
|
FINISHED |
| Object | celebrity-centered reality shows |
—
|
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: celebrity-centered reality shows | Statement: [New York Goes to Hollywood, partOfGenreTrend, celebrity-centered reality shows]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfGenreTrend Context triple: [New York Goes to Hollywood, partOfGenreTrend, celebrity-centered reality shows]
-
A.
genreTrend
chosen
Indicates how the popularity or prevalence of a particular genre changes over time or across contexts.
-
B.
genreWithin
Indicates that one genre is a subgenre or more specific category contained within another, broader genre.
-
C.
targetGenre
Indicates the genre that something is specifically aimed at, categorized under, or intended to belong to.
-
D.
visualGenre
Indicates the visual or stylistic category to which something belongs, such as its artistic or cinematic genre.
-
E.
commonGenre
Indicates that two entities share at least one genre in common.
- 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_69e288ceaab88190899d0acb5931591d |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f27ca38c148190ae65cd692567d43d |
completed | April 29, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f1c43e55688190b55fc20274ed471c |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 17, 2026, 11:37 p.m.