Triple
T22527852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arab cinema |
E556953
|
entity |
| Predicate | hasNotableFilmIndustryCenter |
P133571
|
FINISHED |
| Object | Egyptian cinema |
—
|
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: Egyptian cinema | Statement: [Arab cinema, hasNotableFilmIndustryCenter, Egyptian cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableFilmIndustryCenter Context triple: [Arab cinema, hasNotableFilmIndustryCenter, Egyptian cinema]
-
A.
hasFilmIndustryCenter
Indicates that a location serves as a primary hub or central base for activities related to the film industry.
-
B.
hasNotableCompanyHeadquarters
Indicates that a company’s headquarters is recognized as notable or significant in some way.
-
C.
notableIndustryInArea
chosen
Indicates that a particular industry is especially prominent, significant, or well-known within a given geographic area.
-
D.
hasNotableTown
Indicates that an entity includes or is associated with a town that is considered notable or significant in some way.
-
E.
hasNotableFilm
Indicates that an entity is associated with a film that is considered significant, well-known, or particularly noteworthy.
- 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_69e11e57483c8190b0887c4f8ff26446 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15ed411488190a51320930b9805c2 |
completed | April 29, 2026, 1:28 a.m. |
| PD | Predicate disambiguation | batch_69e898c864148190a3f5feec7967d49c |
completed | April 22, 2026, 9:45 a.m. |
Created at: April 16, 2026, 8:51 p.m.