Triple
T11374927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cinema City |
E269441
|
entity |
| Predicate | competesWith |
P1375
|
FINISHED |
| Object |
Multikino
Multikino is a major Polish multiplex cinema chain operating modern movie theaters across numerous cities in Poland and parts of Europe.
|
E922062
|
NE FINISHED |
How this triple was built (4 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: Multikino | Statement: [Cinema City, competesWith, Multikino]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Multikino Context triple: [Cinema City, competesWith, Multikino]
-
A.
Ikspiari Cinema Complex
Ikspiari Cinema Complex is a multi-screen movie theater located within the Ikspiari shopping and entertainment district at Tokyo Disney Resort in Japan.
-
B.
Cineworld cinema
Cineworld cinema is a major UK multiplex cinema chain offering mainstream film screenings and entertainment facilities.
-
C.
Cinema City
Cinema City is a European cinema chain brand operated by Cineworld Group, known for its multiplex movie theaters across several countries.
-
D.
Regal Cinemas
Regal Cinemas is a major American movie theater chain known for operating multiplex cinemas across the United States.
-
E.
Rave Cinemas
Rave Cinemas is a movie theater chain in the United States that operates multiplex cinemas for mainstream film releases.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Multikino Triple: [Cinema City, competesWith, Multikino]
Generated description
Multikino is a major Polish multiplex cinema chain operating modern movie theaters across numerous cities in Poland and parts of Europe.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Multikino Target entity description: Multikino is a major Polish multiplex cinema chain operating modern movie theaters across numerous cities in Poland and parts of Europe.
-
A.
Ikspiari Cinema Complex
Ikspiari Cinema Complex is a multi-screen movie theater located within the Ikspiari shopping and entertainment district at Tokyo Disney Resort in Japan.
-
B.
Cineworld cinema
Cineworld cinema is a major UK multiplex cinema chain offering mainstream film screenings and entertainment facilities.
-
C.
Cinema City
Cinema City is a European cinema chain brand operated by Cineworld Group, known for its multiplex movie theaters across several countries.
-
D.
Regal Cinemas
Regal Cinemas is a major American movie theater chain known for operating multiplex cinemas across the United States.
-
E.
Rave Cinemas
Rave Cinemas is a movie theater chain in the United States that operates multiplex cinemas for mainstream film releases.
- F. None of above. chosen
Provenance (5 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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea8d244c8190b865260338edb532 |
completed | April 9, 2026, 6:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5568e3e108190ab843236b417f150 |
completed | April 19, 2026, 10:26 p.m. |
| NEDg | Description generation | batch_69e562c7de3c8190befb6d7131129a2c |
completed | April 19, 2026, 11:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e56a776390819082d47ad00cf1862b |
completed | April 19, 2026, 11:51 p.m. |
Created at: April 8, 2026, 9:33 p.m.