Triple

T35735740
Position Surface form Disambiguated ID Type / Status
Subject UGC E1032880 entity
Predicate hasCinemaChain P188138 FINISHED
Object UGC cinemas
UGC cinemas is a major European cinema chain known for operating multiplex movie theaters, particularly in France and neighboring countries.
 E2153731 NE FINISHED

How this triple was built (3 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: UGC cinemas | Statement: [UGC, hasCinemaChain, UGC cinemas]
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: UGC cinemas
Triple: [UGC, hasCinemaChain, UGC cinemas]
Generated description
UGC cinemas is a major European cinema chain known for operating multiplex movie theaters, particularly in France and neighboring countries.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasCinemaChain
Context triple: [UGC, hasCinemaChain, UGC cinemas]
  • A. hasMovieTheater
    Indicates that one entity possesses, contains, or includes a movie theater as part of its facilities or attributes.
  • B. hasNumberOfCinemas
    Indicates the quantity of cinemas associated with a given entity.
  • C. belongsToCinema
    Indicates that something is part of, associated with, or under the ownership/management of a particular cinema.
  • D. hasCinemaOperator
    Indicates that a cinema is operated, managed, or run by a specific organization or individual.
  • E. hasNumberOfTheatres
    Indicates the quantity of theatres associated with or present in a given entity.
  • F. None of above. chosen

Provenance (7 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_69f76e10e59081908d81ad9ce22f40b6 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69fba2877b248190a974eb092243c0c4 completed May 6, 2026, 8:20 p.m.
NED1 Entity disambiguation (via context triple) batch_6a387d21234c81909d16906798d91a87 completed June 22, 2026, 12:09 a.m.
NEDg Description generation batch_6a387dc7ab8081908f48a7e3b3777343 completed June 22, 2026, 12:11 a.m.
NED2 Entity disambiguation (via description) batch_6a3881097d988190824ae388ccb2210d completed June 22, 2026, 12:25 a.m.
PD Predicate disambiguation batch_69fb8d06a1b48190a937aa410d159dfa completed May 6, 2026, 6:48 p.m.
PDg Predicate description generation batch_69fba28684208190921694f23e350c3b completed May 6, 2026, 8:20 p.m.
Created at: May 3, 2026, 4:05 p.m.