Triple

T22505159
Position Surface form Disambiguated ID Type / Status
Subject Rousset E556369 entity
Predicate locatedIn P40 FINISHED
Object Bouches-du-Rhône 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: Bouches-du-Rhône | Statement: [Rousset, locatedIn, Bouches-du-Rhône]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bouches-du-Rhône
Context triple: [Rousset, locatedIn, 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 (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_69e11e555edc81909ca803587dafd747 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15d5bf0f0819093426d83ebd80ef0 completed April 29, 2026, 1:22 a.m.
Created at: April 16, 2026, 8:50 p.m.