Triple

T11067412
Position Surface form Disambiguated ID Type / Status
Subject Ateliers Jean Nouvel E261660 entity
Predicate hasOfficeIn P1268 FINISHED
Object Sydney E8462 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: Sydney | Statement: [Ateliers Jean Nouvel, hasOfficeIn, Sydney]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sydney
Context triple: [Ateliers Jean Nouvel, hasOfficeIn, Sydney]
  • A. Sydney
    Sydney is a recurring character in Alison Bechdel’s long-running comic strip "Dykes to Watch Out For," known for her sharp intellect and complex personal relationships within its ensemble cast.
  • B. Sydney chosen
    Sydney is Australia's largest and most populous city, renowned for its iconic harbour, Opera House, and Harbour Bridge.
  • C. Sydney
    Sydney is the spirited, fashionable young woman who serves as the central heroine of Louisa May Alcott’s novel "An Old-Fashioned Girl."
  • D. Sydney
    Sydney is a unisex given name of Old English origin meaning "wide island" that is used in various English-speaking countries.
  • E. Melbourne
    Melbourne is a major Australian city known for its vibrant arts scene, diverse culture, and status as a leading center for sports and education.
  • 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_69d6aa98650481908609c7c56bfa7902 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d79920428c81908db824ab54e08e8d completed April 9, 2026, 12:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4832d59c08190ab120b991bc8ed3b completed April 19, 2026, 7:24 a.m.
Created at: April 8, 2026, 9:26 p.m.