Triple

T2424160
Position Surface form Disambiguated ID Type / Status
Subject Cineworld Group E53486 entity
Predicate tickerSymbol P1447 FINISHED
Object CINE
CINE is the London Stock Exchange ticker symbol for Cineworld Group, one of the world’s largest cinema chains.
 E265699 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: CINE | Statement: [Cineworld Group, tickerSymbol, CINE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CINE
Context triple: [Cineworld Group, tickerSymbol, CINE]
  • A. Celluloid
    Celluloid is a lightweight, open-source media player for Linux that provides a simple GTK-based interface for the MPV playback engine.
  • B. Le Cinéma
    Le Cinéma is a movie theater within Tokyo’s Bunkamura cultural complex, known for screening a curated selection of domestic and international films.
  • C. FilmScene cinema
    FilmScene cinema is an independent, nonprofit movie theater and film arts organization known for showcasing arthouse, foreign, and documentary films in Iowa City, Iowa.
  • D. Cinemastar
    Cinemastar is a line of hard disk drives produced by HGST, typically designed for consumer and multimedia applications.
  • E. Cinelândia
    Cinelândia is a historic and culturally vibrant square in downtown Rio de Janeiro, Brazil, known for its theaters, cinemas, and early 20th-century architecture.
  • 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: CINE
Triple: [Cineworld Group, tickerSymbol, CINE]
Generated description
CINE is the London Stock Exchange ticker symbol for Cineworld Group, one of the world’s largest cinema chains.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: CINE
Target entity description: CINE is the London Stock Exchange ticker symbol for Cineworld Group, one of the world’s largest cinema chains.
  • A. Celluloid
    Celluloid is a lightweight, open-source media player for Linux that provides a simple GTK-based interface for the MPV playback engine.
  • B. Le Cinéma
    Le Cinéma is a movie theater within Tokyo’s Bunkamura cultural complex, known for screening a curated selection of domestic and international films.
  • C. FilmScene cinema
    FilmScene cinema is an independent, nonprofit movie theater and film arts organization known for showcasing arthouse, foreign, and documentary films in Iowa City, Iowa.
  • D. Cinemastar
    Cinemastar is a line of hard disk drives produced by HGST, typically designed for consumer and multimedia applications.
  • E. Cinelândia
    Cinelândia is a historic and culturally vibrant square in downtown Rio de Janeiro, Brazil, known for its theaters, cinemas, and early 20th-century architecture.
  • 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_69ab495c44d48190b7235b23719bc3f6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc973aee08190b543492f436f3fe5 completed March 7, 2026, 6:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69aebf5e33208190a6899e6672b3daeb completed March 9, 2026, 12:38 p.m.
NEDg Description generation batch_69aec590516c81908f5126e203bb3638 completed March 9, 2026, 1:05 p.m.
NED2 Entity disambiguation (via description) batch_69aec6194a3c81909055a16553f39e78 completed March 9, 2026, 1:07 p.m.
Created at: March 6, 2026, 9:42 p.m.