Triple
T34976038
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krakozhian |
E1008678
|
entity |
| Predicate | legalRecognitionInFilmWorld |
P200268
|
FINISHED |
| Object | temporarily unrecognized due to coup |
—
|
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: temporarily unrecognized due to coup | Statement: [Krakozhian, legalRecognitionInFilmWorld, temporarily unrecognized due to coup]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalRecognitionInFilmWorld Context triple: [Krakozhian, legalRecognitionInFilmWorld, temporarily unrecognized due to coup]
-
A.
reputationInFilm
Indicates the perceived standing or renown an entity has within the context of film, such as its recognition, credibility, or esteem in the film domain.
-
B.
knowsHeIsInAFilmWorld
Indicates that an entity is aware that it exists within a film or movie world rather than a real-world setting.
-
C.
placementInFilm
Indicates the specific position or occurrence of something within the sequence or structure of a film.
-
D.
sangForFilmIndustry
Indicates that a person performed singing specifically for use in the film industry, such as in movies or film soundtracks.
-
E.
occupationInFilm
Indicates that an entity has a specific occupation or role within the context of a particular film.
- 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_69f76dc78a308190a1ac29ad4a9a4895 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ff7dcedab08190a719a707d03306e2 |
completed | May 9, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69ff7d0119348190ad462554e81190fe |
completed | May 9, 2026, 6:29 p.m. |
| PDg | Predicate description generation | batch_69ff7dce25d88190b167a014661bc4ba |
completed | May 9, 2026, 6:32 p.m. |
Created at: May 3, 2026, 4:01 p.m.