Triple
T13267145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regal RPX |
E315951
|
entity |
| Predicate | parentCompany |
P254
|
FINISHED |
| Object | Cineworld Group |
E53486
|
NE 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: Cineworld Group | Statement: [Regal RPX, parentCompany, Cineworld Group]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cineworld Group Context triple: [Regal RPX, parentCompany, Cineworld Group]
-
A.
Cineworld Group
chosen
Cineworld Group is a British-based multinational cinema chain operator that became one of the world’s largest theater companies following its acquisition of Regal Entertainment Group.
-
B.
Cineworld cinema
Cineworld cinema is a major UK multiplex cinema chain offering mainstream film screenings and entertainment facilities.
-
C.
Cinemark Theatres
Cinemark Theatres is a major American movie theater chain operating multiplex cinemas across the United States and in several Latin American countries.
-
D.
United Cinemas
United Cinemas is a Japanese movie theater chain operating multiplex cinemas in various locations, including major shopping and entertainment complexes.
-
E.
Wanda Cinemas
Wanda Cinemas is a major Chinese cinema chain known for operating a large network of modern movie theaters across China.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9901e44bc8190966f87ae219d6bf4 |
completed | April 11, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716cd8c2c8190a28d901fde98dc26 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:25 p.m.