Triple

T3111355
Position Surface form Disambiguated ID Type / Status
Subject Xavier Niel E64957 entity
Predicate name P16 FINISHED
Object Xavier Niel E64957 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: Xavier Niel | Statement: [Xavier Niel, name, Xavier Niel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xavier Niel
Context triple: [Xavier Niel, name, Xavier Niel]
  • A. Xavier Niel chosen
    Xavier Niel is a French billionaire entrepreneur and investor best known as the founder of the telecom company Iliad/Free and a prominent figure in France’s technology and startup ecosystem.
  • B. Philippe Kahn
    Philippe Kahn is a French technology entrepreneur and software engineer best known for founding Borland and inventing the first camera phone solution.
  • C. Michel Virlogeux
    Michel Virlogeux is a renowned French structural engineer and bridge designer known for his work on major long-span bridges around the world.
  • D. Stéphane Boudin
    Stéphane Boudin was a renowned 20th-century French interior designer and president of the firm Maison Jansen, celebrated for his work on high-profile residences and state rooms, including projects for the White House.
  • E. Peter Biziou
    Peter Biziou is a British cinematographer known for his work on films such as "Bugsy Malone" and the Oscar-winning "Mississippi Burning."
  • 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_69ad857eeaf48190b34ebfdaa7a264cf completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada43954f0819096a96331bf3c53a8 completed March 8, 2026, 4:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b20394a8cc8190b114760079f8b0f6 completed March 12, 2026, 12:06 a.m.
Created at: March 8, 2026, 3:04 p.m.