Triple
T3790452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MacArthur Park |
E89631
|
entity |
| Predicate | hasRepresentationInFilm |
P22734
|
FINISHED |
| Object | various films set in Los Angeles |
—
|
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: various films set in Los Angeles | Statement: [MacArthur Park, hasRepresentationInFilm, various films set in Los Angeles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRepresentationInFilm Context triple: [MacArthur Park, hasRepresentationInFilm, various films set in Los Angeles]
-
A.
includedInFilm
chosen
Indicates that one entity (such as a scene, segment, or element) is contained within or forms part of a particular film.
-
B.
partOfFilmographyOf
Indicates that a work (such as a film, show, or role) is included in the body of screen-related works credited to a particular person.
-
C.
hasNotableFilm
Indicates that an entity is associated with a film that is considered significant, well-known, or particularly noteworthy.
-
D.
producedFilmStarring
Indicates that a person or company produced a film in which a specified actor or set of actors starred.
-
E.
producedFilm
Indicates that one entity served as the producer (or production company) responsible for making or financing the creation of a particular film.
- 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_69aed9597d6881909b6ee3b9de859223 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecefa3608190a7a20ed6df6a64b2 |
completed | March 9, 2026, 3:53 p.m. |
| PD | Predicate disambiguation | batch_69aee743c8d08190a9f9c97b836bd703 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:15 p.m.