Triple
T1255872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Contemporary World Cinema |
E26989
|
entity |
| Predicate | screeningFormat |
P8484
|
FINISHED |
| Object | theatrical screenings |
—
|
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: theatrical screenings | Statement: [Contemporary World Cinema, screeningFormat, theatrical screenings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screeningFormat Context triple: [Contemporary World Cinema, screeningFormat, theatrical screenings]
-
A.
screeningType
Indicates the specific method or category of screening applied in a screening process or evaluation.
-
B.
filmPerforationType
Indicates the specific style or pattern of perforations used along the edges of a film stock.
-
C.
filmType
Indicates the specific category or genre that a film belongs to.
-
D.
servedInTheatres
chosen
Indicates that a film or performance was publicly exhibited in movie theaters or similar cinema venues.
-
E.
hasFilmColorType
Indicates that a film is associated with a particular color process or color classification (e.g., color, black-and-white).
- 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_69a49487a9c48190ba9b05348fd1b53f |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bfa726548190911b4022dc1be3c3 |
completed | March 1, 2026, 10:37 p.m. |
| PD | Predicate disambiguation | batch_69a4bb6c977c8190a2bf3e8b67a59beb |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:47 p.m.