Triple
T36565836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rita Hayworth and Fred Astaire |
E901972
|
entity |
| Predicate | typicalSettingOfFilms |
P52439
|
FINISHED |
| Object | contemporary urban environments |
—
|
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: contemporary urban environments | Statement: [Rita Hayworth and Fred Astaire, typicalSettingOfFilms, contemporary urban environments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSettingOfFilms Context triple: [Rita Hayworth and Fred Astaire, typicalSettingOfFilms, contemporary urban environments]
-
A.
livesInFilmSetting
Indicates that an entity resides or exists within the fictional world or setting depicted in a particular film.
-
B.
filmSceneType
Indicates the type or category of a scene within a film, such as its narrative function, style, or setting.
-
C.
filmSetting
chosen
Indicates the place, time, or environment in which the events of a film are set or take place.
-
D.
producedFilmSetIn
Indicates that a producer or production entity created a film whose story is set in a particular location or setting.
-
E.
associatedWithFictionalSetting
Indicates that an entity has a connection or relevance to a particular fictional setting or universe.
- 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_69f76e6416708190a9754b8c52d4e453 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:11 p.m.