Triple
T17692456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dendy Films |
E441064
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object |
Dendy Cinemas
Dendy Cinemas is an Australian boutique cinema chain known for showcasing independent, arthouse, and foreign films.
|
E1282180
|
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: Dendy Cinemas | Statement: [Dendy Films, associatedWith, Dendy Cinemas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dendy Cinemas Context triple: [Dendy Films, associatedWith, Dendy Cinemas]
-
A.
Golden Harvest Cinemas
Golden Harvest Cinemas is a movie theater chain operated by the Hong Kong-based film company Golden Harvest, known for running multiplex cinemas across Asia.
-
B.
Wanda Cinemas
Wanda Cinemas is a major Chinese cinema chain known for operating a large network of modern movie theaters across China.
-
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.
Multikino
Multikino is a major Polish multiplex cinema chain operating modern movie theaters across numerous cities in Poland and parts of Europe.
-
E.
Regal Cinemas
Regal Cinemas is a major American movie theater chain known for operating multiplex cinemas across the United States.
- 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: Dendy Cinemas Triple: [Dendy Films, associatedWith, Dendy Cinemas]
Generated description
Dendy Cinemas is an Australian boutique cinema chain known for showcasing independent, arthouse, and foreign films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dendy Cinemas Target entity description: Dendy Cinemas is an Australian boutique cinema chain known for showcasing independent, arthouse, and foreign films.
-
A.
Golden Harvest Cinemas
Golden Harvest Cinemas is a movie theater chain operated by the Hong Kong-based film company Golden Harvest, known for running multiplex cinemas across Asia.
-
B.
Wanda Cinemas
Wanda Cinemas is a major Chinese cinema chain known for operating a large network of modern movie theaters across China.
-
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.
Multikino
Multikino is a major Polish multiplex cinema chain operating modern movie theaters across numerous cities in Poland and parts of Europe.
-
E.
Regal Cinemas
Regal Cinemas is a major American movie theater chain known for operating multiplex cinemas across the United States.
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47153a5c8819095c36fd414167fb1 |
completed | April 19, 2026, 6:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a022331e1388190b596c7867f1c5b60 |
completed | May 11, 2026, 6:42 p.m. |
| NEDg | Description generation | batch_6a022661afd88190b57c338973571cb7 |
completed | May 11, 2026, 6:56 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a022705b76481909f77bedfb9d5d30d |
completed | May 11, 2026, 6:59 p.m. |
Created at: April 10, 2026, 10:03 a.m.