Triple
T10395705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suite from 'Talk to Her' |
E245007
|
entity |
| Predicate | originalLanguageOfFilmScore |
P93689
|
FINISHED |
| Object | Spanish |
—
|
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: Spanish | Statement: [Suite from 'Talk to Her', originalLanguageOfFilmScore, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalLanguageOfFilmScore Context triple: [Suite from 'Talk to Her', originalLanguageOfFilmScore, Spanish]
-
A.
originalLanguageOfFilmOrTVShow
Indicates the language in which a film or TV show was originally produced and released.
-
B.
originalTitleLanguage
Indicates the language in which a work’s original title was written or expressed.
-
C.
originalLanguageCountry
Indicates the country where a work’s original language is primarily spoken or officially used.
-
D.
originalLanguageOfWinningWorks
Indicates the language in which the works that won an award or competition were originally created or written.
-
E.
notableLanguageOfEligibleFilms
Indicates that there is a notable language associated with the set of films that qualify as eligible under a given criterion or program.
- 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9ce6bb08190bfeaba98a126526d |
completed | April 7, 2026, 11:26 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb438c481908dff87c47de2f069 |
completed | April 7, 2026, 10:43 a.m. |
| PDg | Predicate description generation | batch_69d4e944fac4819093b0312aa0efd729 |
completed | April 7, 2026, 11:23 a.m. |
Created at: April 6, 2026, 12:06 p.m.