Triple
T13799679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Filmstaden Bergakungen |
E331605
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object |
Filmstaden chain
Filmstaden chain is a major Swedish cinema chain operating multiplex movie theaters across the country.
|
E1062023
|
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: Filmstaden chain | Statement: [Filmstaden Bergakungen, brand, Filmstaden chain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Filmstaden chain Context triple: [Filmstaden Bergakungen, brand, Filmstaden chain]
-
A.
Fox Theatres chain
The Fox Theatres chain was a historic group of lavish movie palaces in the United States developed by film pioneer William Fox’s Fox Film Corporation during the early 20th century.
-
B.
Main Street Cinema
Main Street Cinema is a nostalgic, early-20th-century-style movie theater attraction in Disney theme parks that typically showcases classic Disney cartoons and film clips.
-
C.
Multikino
Multikino is a major Polish multiplex cinema chain operating modern movie theaters across numerous cities in Poland and parts of Europe.
-
D.
Reel Cinemas
Reel Cinemas is a popular cinema chain in Dubai known for its modern multiplex theaters and premium movie-going experiences.
-
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. 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: Filmstaden chain Triple: [Filmstaden Bergakungen, brand, Filmstaden chain]
Generated description
Filmstaden chain is a major Swedish cinema chain operating multiplex movie theaters across the country.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Filmstaden chain Target entity description: Filmstaden chain is a major Swedish cinema chain operating multiplex movie theaters across the country.
-
A.
Fox Theatres chain
The Fox Theatres chain was a historic group of lavish movie palaces in the United States developed by film pioneer William Fox’s Fox Film Corporation during the early 20th century.
-
B.
Main Street Cinema
Main Street Cinema is a nostalgic, early-20th-century-style movie theater attraction in Disney theme parks that typically showcases classic Disney cartoons and film clips.
-
C.
Multikino
Multikino is a major Polish multiplex cinema chain operating modern movie theaters across numerous cities in Poland and parts of Europe.
-
D.
Reel Cinemas
Reel Cinemas is a popular cinema chain in Dubai known for its modern multiplex theaters and premium movie-going experiences.
-
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. 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_69d81c58feb08190a77bca8bf7d6d20f |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de025ce9148190b23370f6a522ff7a |
completed | April 14, 2026, 9:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b0893a20819081d4001b8dbc9c36 |
completed | May 3, 2026, 8:31 p.m. |
| NEDg | Description generation | batch_69f7b138fda88190b2b7ffb51ce02a40 |
completed | May 3, 2026, 8:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7b28ca218819097fc35042d3b278a |
completed | May 3, 2026, 8:39 p.m. |
Created at: April 9, 2026, 10:11 p.m.