Triple

T2377417
Position Surface form Disambiguated ID Type / Status
Subject HGST E46228 entity
Predicate hasBrand P1500 FINISHED
Object Cinemastar
Cinemastar is a line of hard disk drives produced by HGST, typically designed for consumer and multimedia applications.
 E260672 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: Cinemastar | Statement: [HGST, hasBrand, Cinemastar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Cinemastar
Context triple: [HGST, hasBrand, Cinemastar]
  • A. Cinesphere
    Cinesphere is a landmark domed cinema in Toronto, Canada, known as the world’s first permanent IMAX theatre and a key architectural feature of the Ontario Place complex.
  • B. Yara Cinema
    Yara Cinema is a prominent and historic movie theater in Havana, Cuba, known as a cultural landmark and popular gathering place in the Vedado district.
  • C. Reel Cinemas
    Reel Cinemas is a popular cinema chain in Dubai known for its modern multiplex theaters and premium movie-going experiences.
  • D. Regal Cinemas
    Regal Cinemas is a major American movie theater chain known for operating multiplex cinemas across the United States.
  • E. Cinema de Lux
    Cinema de Lux is a premium movie theater complex known for offering an upscale cinema experience with enhanced amenities and comfort.
  • 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: Cinemastar
Triple: [HGST, hasBrand, Cinemastar]
Generated description
Cinemastar is a line of hard disk drives produced by HGST, typically designed for consumer and multimedia applications.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Cinemastar
Target entity description: Cinemastar is a line of hard disk drives produced by HGST, typically designed for consumer and multimedia applications.
  • A. Cinesphere
    Cinesphere is a landmark domed cinema in Toronto, Canada, known as the world’s first permanent IMAX theatre and a key architectural feature of the Ontario Place complex.
  • B. Yara Cinema
    Yara Cinema is a prominent and historic movie theater in Havana, Cuba, known as a cultural landmark and popular gathering place in the Vedado district.
  • C. Reel Cinemas
    Reel Cinemas is a popular cinema chain in Dubai known for its modern multiplex theaters and premium movie-going experiences.
  • D. Regal Cinemas
    Regal Cinemas is a major American movie theater chain known for operating multiplex cinemas across the United States.
  • E. Cinema de Lux
    Cinema de Lux is a premium movie theater complex known for offering an upscale cinema experience with enhanced amenities and comfort.
  • 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_69a88a1554a48190a0180682bcf099be completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abc79610e8819084abfbccd1dc67c0 completed March 7, 2026, 6:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea8af12bc8190a6667beb729406a3 completed March 9, 2026, 11:02 a.m.
NEDg Description generation batch_69aeab34e4d0819088a24121785f81c6 completed March 9, 2026, 11:12 a.m.
NED2 Entity disambiguation (via description) batch_69aeabcae91c81908b2197ce0ef7eaa9 completed March 9, 2026, 11:15 a.m.
Created at: March 4, 2026, 7:57 p.m.