Triple

T1283665
Position Surface form Disambiguated ID Type / Status
Subject Vaucluse E27382 entity
Predicate contains P35 FINISHED
Object Orange E3952 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: Orange | Statement: [Vaucluse, contains, Orange]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Orange
Context triple: [Vaucluse, contains, Orange]
  • A. Orange
    Orange is a major French multinational telecommunications company providing mobile, internet, and other digital services across numerous countries.
  • B. Orange chosen
    Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
  • C. Orange
    Orange is a regional city in the Central Tablelands of New South Wales, Australia, known for its cool-climate wines, agriculture, and growing tourism industry.
  • D. Brown
    Brown is a common English-language surname of Anglo-Saxon origin, typically derived from a nickname referring to hair color, complexion, or clothing.
  • E. Crome Yellow
    Crome Yellow is Aldous Huxley’s satirical 1921 novel that lampoons English upper-class society and intellectual pretensions through the events at a country house gathering.
  • 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_69a496d3710c8190955dee8bc0dacb50 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c0b599ac819096fca9ada294d939 completed March 1, 2026, 10:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69aca2fdb3ac81909bc836e2a655130c completed March 7, 2026, 10:13 p.m.
Created at: March 1, 2026, 7:50 p.m.