Triple

T1035960
Position Surface form Disambiguated ID Type / Status
Subject Ajaccio E22361 entity
Predicate connectsToByFerry P1831 FINISHED
Object Toulon E83956 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: Toulon | Statement: [Ajaccio, connectsToByFerry, Toulon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toulon
Context triple: [Ajaccio, connectsToByFerry, Toulon]
  • A. Toulon chosen
    Toulon is a major port city on France’s Mediterranean coast that serves as the principal base of the French Navy.
  • B. La Rochelle
    La Rochelle is a historic French Atlantic port city that became a major stronghold and refuge for Huguenots during the French Wars of Religion.
  • C. Toulouse
    Toulouse is a major city in southwestern France known for its aerospace industry, historic pink-brick architecture, and vibrant university and cultural life.
  • D. Marseille
    Marseille is a historic Mediterranean port city in southern France known for its diverse culture, maritime heritage, and role as a major economic hub.
  • E. Nantes
    Nantes is a historic port city in western France on the Loire River, known for its maritime heritage, cultural institutions, and vibrant arts scene.
  • 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_69a493d848848190aed4011b34b2e8d3 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bb71e7f88190bf33bbe5ef2c68ff completed March 1, 2026, 10:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae30355a548190af3f858170b9b82c completed March 9, 2026, 2:28 a.m.
Created at: March 1, 2026, 7:41 p.m.