Triple
T36565837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rita Hayworth and Fred Astaire |
E901972
|
entity |
| Predicate | typicalToneOfFilms |
P148164
|
FINISHED |
| Object | light-hearted |
—
|
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: light-hearted | Statement: [Rita Hayworth and Fred Astaire, typicalToneOfFilms, light-hearted]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalToneOfFilms Context triple: [Rita Hayworth and Fred Astaire, typicalToneOfFilms, light-hearted]
-
A.
targetAudienceOfFilms
Indicates the group of people or demographic segment that a particular film is primarily intended or designed to appeal to.
-
B.
typicalGenresIncluded
Indicates that certain genres are commonly or characteristically included as part of another entity’s usual set of genres.
-
C.
filmTypeContext
Indicates the contextual relationship between a film and its type or category within a specific classification or usage setting.
-
D.
toneCategory
chosen
Indicates the tonal classification or type assigned to an entity, such as its pitch pattern, mood, or prosodic category.
-
E.
keyGenreFilm
Indicates that a particular genre is the primary or defining genre associated with a given 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_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.