Triple
T2424179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cineworld Group |
E53486
|
entity |
| Predicate | becameOneOfWorldsLargestCinemaChains |
P38482
|
FINISHED |
| Object | after acquisition of Regal Entertainment Group |
—
|
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: after acquisition of Regal Entertainment Group | Statement: [Cineworld Group, becameOneOfWorldsLargestCinemaChains, after acquisition of Regal Entertainment Group]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: becameOneOfWorldsLargestCinemaChains Context triple: [Cineworld Group, becameOneOfWorldsLargestCinemaChains, after acquisition of Regal Entertainment Group]
-
A.
hasNumberOfCinemas
Indicates the quantity of cinemas associated with a given entity.
-
B.
franchiseEventuallyBecame
Indicates that one franchise transformed into, was succeeded by, or ultimately came to be identified as another franchise over time.
-
C.
primaryCinema
Indicates that one entity is the main or most significant cinema associated with another entity (such as a person, work, or event).
-
D.
hasNumberOfTheatres
Indicates the quantity of theatres associated with or present in a given entity.
-
E.
servedInTheatres
Indicates that a film or performance was publicly exhibited in movie theaters or similar cinema venues.
- 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_69ab495c44d48190b7235b23719bc3f6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abc9f342e88190a430b02842ded418 |
completed | March 7, 2026, 6:47 a.m. |
| PD | Predicate disambiguation | batch_69abc5a889948190b77de4ef6ac815a8 |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abc9f1ba608190b488874bed3533dd |
completed | March 7, 2026, 6:47 a.m. |
Created at: March 6, 2026, 9:42 p.m.