Triple

T13697889
Position Surface form Disambiguated ID Type / Status
Subject Marignane E328434 entity
Predicate near P350 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: [Marignane, near, Marseille]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marseille
Context triple: [Marignane, near, 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. Lyon
    Lyon is a historic Scottish noble family name most prominently associated with the Earls of Strathmore and Kinghorne.
  • D. Toulon
    Toulon is a major port city on France’s Mediterranean coast that serves as the principal base of the French Navy.
  • E. 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.
  • 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_69d8076ff62081908a7bd79889edd7a0 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc878b57c819094e7ea6d1a64211f completed April 12, 2026, 4:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdf06a3e208190a24e6cd9cb97e99c completed May 8, 2026, 2:17 p.m.
Created at: April 9, 2026, 9:54 p.m.