Triple

T7524787
Position Surface form Disambiguated ID Type / Status
Subject Oscar de la Renta E177863 entity
Predicate workedFor P1910 FINISHED
Object Elizabeth Arden E1396 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: Elizabeth Arden | Statement: [Oscar de la Renta, workedFor, Elizabeth Arden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth Arden
Context triple: [Oscar de la Renta, workedFor, Elizabeth Arden]
  • A. Elizabeth Arden chosen
    Elizabeth Arden was a pioneering Canadian-American businesswoman who founded the Elizabeth Arden cosmetics empire and helped shape the modern beauty industry.
  • B. Lancôme
    Lancôme is a French luxury cosmetics and skincare brand renowned for its high-end perfumes, makeup, and beauty products.
  • C. Yves Saint Laurent Beauté
    Yves Saint Laurent Beauté is a luxury cosmetics and fragrance brand known for its high-end makeup, skincare, and iconic perfumes.
  • D. Diane Jergens
    Diane Jergens was an American film and television actress active in the 1950s and 1960s, known for her roles in teen and drama films.
  • E. Armani Beauty
    Armani Beauty is the cosmetics and fragrance line of the Giorgio Armani fashion house, known for its luxurious makeup, skincare, and signature perfumes.
  • 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_69c69f29bf3081909a146aec7755f185 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f7c61b508190b582f54ecbb387e3 completed March 27, 2026, 9:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c84631e6bc819099b3a7819c3ae9a7 completed March 28, 2026, 9:20 p.m.
Created at: March 27, 2026, 3:46 p.m.