Triple
T1666196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanghai International Film Festival |
E36016
|
entity |
| Predicate | screeningVenues |
P25526
|
FINISHED |
| Object | cinemas in Shanghai |
—
|
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: cinemas in Shanghai | Statement: [Shanghai International Film Festival, screeningVenues, cinemas in Shanghai]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screeningVenues Context triple: [Shanghai International Film Festival, screeningVenues, cinemas in Shanghai]
-
A.
typicalVenues
chosen
Indicates that the specified locations are common or standard places where the associated activity, event, or entity usually occurs or is hosted.
-
B.
venueSelection
Indicates the relationship in which a specific venue is chosen or designated for an event, activity, or purpose among available options.
-
C.
primaryVenues
Indicates the main or most important venues associated with or used by a given entity.
-
D.
hasEntertainmentVenue
Indicates that an entity possesses, contains, or is associated with an entertainment venue as part of its facilities or offerings.
-
E.
hasNumberOfCinemas
Indicates the quantity of cinemas associated with a given entity.
- 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_69a8861286808190939afff3ce8ee31e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa994f92b0819084ee2f6a672334b9 |
completed | March 6, 2026, 9:07 a.m. |
| PD | Predicate disambiguation | batch_69a907d2475c8190b7ec7dccd3335eb1 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:29 p.m.