Triple

T3081337
Position Surface form Disambiguated ID Type / Status
Subject Bernard Arnault E64261 entity
Predicate businessInterest P3849 FINISHED
Object Moët & Chandon E9584 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: Moët & Chandon | Statement: [Bernard Arnault, businessInterest, Moët & Chandon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Moët & Chandon
Context triple: [Bernard Arnault, businessInterest, Moët & Chandon]
  • A. Pernod Ricard
    Pernod Ricard is a major French wine and spirits company known globally for brands such as Absolut, Jameson, and Chivas Regal.
  • B. Barillot & Fils
    Barillot & Fils was an 18th-century French publishing house best known for issuing Montesquieu’s influential political treatise "The Spirit of the Laws."
  • C. Champagne chosen
    Champagne is a renowned wine-producing region in northeastern France famous for its sparkling wines made primarily from Chardonnay, Pinot Noir, and Pinot Meunier grapes.
  • D. Les Vins de France
    Les Vins de France is a wine-focused retail shop located in the France Pavilion, offering a curated selection of French wines and related products.
  • E. Hennessy
    Hennessy is a surname most prominently associated with John L. Hennessy, a renowned computer scientist and former president of Stanford University.
  • 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_69ad857bb4c88190a4cf27893fcabed8 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada1aaf6d48190af4f9106965589b0 completed March 8, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f89443a4819091dafc560b45cc26 completed March 11, 2026, 11:19 p.m.
Created at: March 8, 2026, 3:03 p.m.