Triple
T24429508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seth Green as Dan Mott |
E615951
|
entity |
| Predicate | studioClassification |
P155889
|
FINISHED |
| Object | Hollywood studio 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: Hollywood studio comedy | Statement: [Seth Green as Dan Mott, studioClassification, Hollywood studio comedy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: studioClassification Context triple: [Seth Green as Dan Mott, studioClassification, Hollywood studio comedy]
-
A.
genreOfAppearance
Indicates the genre or type of creative work in which an entity appears.
-
B.
visualGenre
Indicates the visual or stylistic category to which something belongs, such as its artistic or cinematic genre.
-
C.
studioFilm
Indicates that a film is produced, distributed, or otherwise created by a particular studio.
-
D.
filmGenreOfRelatedWork
Indicates that a work is related to another work through sharing or being associated with the same film genre.
-
E.
cinemaCategory
Indicates the classification or genre category assigned to a cinema or film.
- 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_69e2d7eadb248190a867130fe45f0388 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f296aab8948190b9cb869bab71fb4c |
completed | April 29, 2026, 11:39 p.m. |
| PD | Predicate disambiguation | batch_69f287cc4fd4819081e93cc638d9512d |
completed | April 29, 2026, 10:35 p.m. |
| PDg | Predicate description generation | batch_69f2915233c48190a181c8c1924e892c |
completed | April 29, 2026, 11:16 p.m. |
Created at: April 18, 2026, 2:15 a.m.