Triple

T8534751
Position Surface form Disambiguated ID Type / Status
Subject London–Marseille E202049 entity
Predicate endPoint P390 FINISHED
Object Marseille, France 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, France | Statement: [London–Marseille, endPoint, Marseille, France]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marseille, France
Context triple: [London–Marseille, endPoint, Marseille, France]
  • 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. Montpellier, France
    Montpellier, France is a historic and vibrant city in southern France near the Mediterranean coast, known for its medieval architecture, large student population, and role as a regional cultural and economic 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. City of Nice
    The City of Nice is a major coastal city on the French Riviera, renowned for its Mediterranean climate, historic old town, and rich artistic and cultural heritage.
  • E. La Ciotat
    La Ciotat is a coastal town on the Mediterranean in southern France, known for its historic shipyards and as one of the birthplaces of early cinema through the Lumière brothers’ pioneering films.
  • 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_69ca832355b08190b8b6a4ab4a4a3554 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe6a0ccd4819097b41d0dfb1c5018 completed March 31, 2026, 3:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69cebb71c81881909e7b9e84d2601949 completed April 2, 2026, 6:54 p.m.
Created at: March 30, 2026, 6:17 p.m.