Triple
T29471929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Служебный роман |
E747531
|
entity |
| Predicate | язык фильма |
P93753
|
FINISHED |
| Object | русский язык |
—
|
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: русский язык | Statement: [Служебный роман, язык фильма, русский язык]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: язык фильма Context triple: [Служебный роман, язык фильма, русский язык]
-
A.
basedInFilmLanguage
Indicates that something is created, presented, or expressed using the language employed in a particular film.
-
B.
filmedInLanguage
Indicates that a film or video work was originally recorded using a particular spoken or signed language.
-
C.
primaryFilmingLanguage
chosen
Indicates the main language in which a film or audiovisual work was originally filmed or recorded.
-
D.
basedOnFilmLanguage
Indicates that something is derived from, adapted from, or otherwise grounded in the language used in a particular film.
-
E.
filmLanguageFormat
Indicates the specific language and presentation format (e.g., dubbed, subtitled, original audio) in which a film is released or available.
- 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_69f0bd42cf308190bb01b20bc5b7c2d0 |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69f66babf5e08190b8e1007546f3881a |
completed | May 2, 2026, 9:25 p.m. |
| PD | Predicate disambiguation | batch_69f66339175c819080bd70f0ff7057b1 |
completed | May 2, 2026, 8:48 p.m. |
Created at: April 28, 2026, 3:57 p.m.