Triple

T4014591
Position Surface form Disambiguated ID Type / Status
Subject Gard E90724 entity
Predicate borders P224 FINISHED
Object Bouches-du-Rhône E101140 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: Bouches-du-Rhône | Statement: [Gard, borders, Bouches-du-Rhône]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bouches-du-Rhône
Context triple: [Gard, borders, Bouches-du-Rhône]
  • A. Bouches-du-Rhône chosen
    Bouches-du-Rhône is a department in southern France known for the city of Marseille, its Mediterranean coastline, and parts of the historic Provence region.
  • B. Alpes-Maritimes
    Alpes-Maritimes is a department in southeastern France on the Mediterranean coast, known for the French Riviera cities of Nice and Cannes and its mix of coastal and Alpine landscapes.
  • C. Comtat Venaissin
    Comtat Venaissin was a historic papal enclave in southeastern France that, together with Avignon, formed a key center of papal temporal power from the Middle Ages until the French Revolution.
  • D. Hérault
    Hérault is a department in southern France known for its Mediterranean coastline, vineyards, and historic cities such as Montpellier and Béziers.
  • E. Lozère
    Lozère is a sparsely populated department in southern France known for its rugged landscapes, including parts of the Cévennes and numerous river valleys.
  • 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_69aed95e44088190aff7d90a151b1b20 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefaa5afdc8190b709af2473d75d02 completed March 9, 2026, 4:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5e4d20dd0819080773876f6198250 completed March 14, 2026, 10:44 p.m.
Created at: March 9, 2026, 3:35 p.m.