Triple

T7948689
Position Surface form Disambiguated ID Type / Status
Subject Glamour E184558 entity
Predicate hasEdition P35 FINISHED
Object Glamour France E184558 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: Glamour France | Statement: [Glamour, hasEdition, Glamour France]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Glamour France
Context triple: [Glamour, hasEdition, Glamour France]
  • A. Mademoiselle magazine
    Mademoiselle magazine was an American women’s fashion and lifestyle magazine known for its literary quality and for publishing notable fiction and essays by emerging writers.
  • B. Marie Claire
    Marie Claire is an international women’s magazine known for combining fashion and beauty coverage with in-depth features on culture, politics, and social issues.
  • C. Glamour magazine chosen
    Glamour magazine is a long-running women’s lifestyle and fashion publication known for its coverage of beauty, culture, and influential women.
  • D. The Vogue
    The Vogue was a pivotal Seattle nightclub that became a key hub for the emerging grunge scene in the late 1980s and early 1990s.
  • E. Glamour
    Glamour is a popular international women's magazine known for its coverage of fashion, beauty, lifestyle, and celebrity culture.
  • 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_69ca8291c2008190b1b8832c87814bcf completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3b2bf6f48190ac7491c41045cab2 completed March 31, 2026, 3:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbe03c7d308190aec1172415be995c completed March 31, 2026, 2:54 p.m.
Created at: March 30, 2026, 5:10 p.m.