Triple

T14837646
Position Surface form Disambiguated ID Type / Status
Subject Gieves & Hawkes E348872 entity
Predicate foundedAs P364 FINISHED
Object Gieves E348872 NE FINISHED

How this triple was built (2 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: Gieves | Statement: [Gieves & Hawkes, foundedAs, Gieves]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gieves
Context triple: [Gieves & Hawkes, foundedAs, Gieves]
  • A. Gieves & Hawkes chosen
    Gieves & Hawkes is a prestigious British bespoke and ready-to-wear tailor and menswear brand, renowned for its military heritage and long-standing presence at No. 1 Savile Row in London.
  • B. Burberry
    Burberry is a British luxury fashion house renowned for its iconic trench coats, distinctive check pattern, and heritage-inspired apparel and accessories.
  • C. Brooks Brothers
    Brooks Brothers is a historic American clothing brand renowned for its classic menswear, preppy style, and status as one of the oldest apparel retailers in the United States.
  • D. Ben Sherman
    Ben Sherman is a rookie Los Angeles police officer and central protagonist in the television drama series "Southland," known for his moral struggles and on-the-job maturation.
  • E. Ted Baker
    Ted Baker is a fictional character who appears in the 1989 romantic fantasy film "Always."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d822ec69008190a9232caa68836872 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded28d0ddc8190a34e3e2d469ab762 completed April 14, 2026, 11:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe38a813b881908a71350073c8fc5c completed May 8, 2026, 7:25 p.m.
Created at: April 10, 2026, 1:52 a.m.