Triple

T3046283
Position Surface form Disambiguated ID Type / Status
Subject Spiral Building E83455 entity
Predicate owner P347 FINISHED
Object Wacoal
Wacoal is a Japanese company best known as a leading manufacturer and retailer of women's lingerie and intimate apparel.
 E323147 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: Wacoal | Statement: [Spiral Building, owner, Wacoal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wacoal
Context triple: [Spiral Building, owner, Wacoal]
  • A. Isabel Jeans
    Isabel Jeans was a British stage and film actress known for her sophisticated roles in early 20th-century cinema, including appearances in several Alfred Hitchcock films.
  • B. Bata
    Bata is a major port city on the mainland of Equatorial Guinea, serving as a key economic and transportation hub for the country.
  • C. Semler
    Semler is a German surname most notably associated with Johann Salomo Semler, an influential 18th-century biblical scholar and theologian.
  • D. Ursula Jeans
    Ursula Jeans was a British stage and film actress known for her versatile character roles in mid-20th-century cinema and theatre.
  • E. Trottiera
    Trottiera is one of the traditional bells housed in St Mark's Campanile in Venice, historically used to signal specific civic or religious events.
  • 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: Wacoal
Triple: [Spiral Building, owner, Wacoal]
Generated description
Wacoal is a Japanese company best known as a leading manufacturer and retailer of women's lingerie and intimate apparel.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wacoal
Target entity description: Wacoal is a Japanese company best known as a leading manufacturer and retailer of women's lingerie and intimate apparel.
  • A. Isabel Jeans
    Isabel Jeans was a British stage and film actress known for her sophisticated roles in early 20th-century cinema, including appearances in several Alfred Hitchcock films.
  • B. Bata
    Bata is a major port city on the mainland of Equatorial Guinea, serving as a key economic and transportation hub for the country.
  • C. Semler
    Semler is a German surname most notably associated with Johann Salomo Semler, an influential 18th-century biblical scholar and theologian.
  • D. Ursula Jeans
    Ursula Jeans was a British stage and film actress known for her versatile character roles in mid-20th-century cinema and theatre.
  • E. Trottiera
    Trottiera is one of the traditional bells housed in St Mark's Campanile in Venice, historically used to signal specific civic or religious events.
  • 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_69ad8b24924c8190a9bb6f61d519e4ae completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9bab541c8190a17aca26b3dcfae7 completed March 8, 2026, 3:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1eef43da8819094cd438c93c64c86 completed March 11, 2026, 10:38 p.m.
NEDg Description generation batch_69b1f071a53481908fb1d7b9803c65a6 completed March 11, 2026, 10:45 p.m.
NED2 Entity disambiguation (via description) batch_69b1f16489e881909368470bb53a900c completed March 11, 2026, 10:49 p.m.
Created at: March 8, 2026, 3:01 p.m.