Triple

T17847742
Position Surface form Disambiguated ID Type / Status
Subject Cap Corse E445707 entity
Predicate hasSettlement P1068 FINISHED
Object Saint-Florent NE NERFINISHED

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: Saint-Florent | Statement: [Cap Corse, hasSettlement, Saint-Florent]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saint-Florent
Context triple: [Cap Corse, hasSettlement, Saint-Florent]
  • A. Saint-Florent chosen
    Saint-Florent is a picturesque coastal commune and popular yachting and beach resort on the northern coast of Corsica, France.
  • B. Saint-Florentin
    Saint-Florentin is a small commune in north-central France known for its historic church and location at the confluence of the Armance and Armançon rivers.
  • C. Frontignan
    Frontignan is a coastal commune in southern France known for its Muscat wine production and Mediterranean setting near Sète.
  • D. Vert-le-Grand
    Vert-le-Grand is a small commune in the Essonne department in the Île-de-France region of northern France.
  • E. Marignane
    Marignane is a commune in southern France near Marseille, known for hosting Marseille Provence Airport and its proximity to the Mediterranean coast.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9f26f18819089c9e43250bee6ae completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e48ffc5fec8190adc66f7b0e264d5f completed April 19, 2026, 8:19 a.m.
Created at: April 10, 2026, 10:16 a.m.