Triple

T10331341
Position Surface form Disambiguated ID Type / Status
Subject Lyon–Marseille axis E242879 entity
Predicate servesCity P82 FINISHED
Object Marseille E15143 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: Marseille | Statement: [Lyon–Marseille axis, servesCity, Marseille]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marseille
Context triple: [Lyon–Marseille axis, servesCity, Marseille]
  • A. Marseille chosen
    Marseille is a historic Mediterranean port city in southern France known for its diverse culture, maritime heritage, and role as a major economic hub.
  • B. Lyon
    Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
  • C. Toulon
    Toulon is a major port city on France’s Mediterranean coast that serves as the principal base of the French Navy.
  • D. Marseille metropolitan area
    The Marseille metropolitan area is a major urban and economic hub in southern France centered on the port city of Marseille and its surrounding communes along the Mediterranean coast.
  • E. Aix-en-Provence
    Aix-en-Provence is a historic and picturesque city in southern France, renowned for its Provençal charm, fountains, and as the hometown of painter Paul Cézanne.
  • 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_69d381af787481908bc401325c760a88 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d7fcaf4c8190bdcfcb953d88ea2f completed April 7, 2026, 10:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d94aec37288190bd9060ba39ec4df2 completed April 10, 2026, 7:09 p.m.
Created at: April 6, 2026, 11:52 a.m.