Triple

T3531718
Position Surface form Disambiguated ID Type / Status
Subject Southern France E74674 entity
Predicate hasSubregion P285 FINISHED
Object Alpes-Maritimes E73900 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: Alpes-Maritimes | Statement: [Southern France, hasSubregion, Alpes-Maritimes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alpes-Maritimes
Context triple: [Southern France, hasSubregion, Alpes-Maritimes]
  • A. Alpes-Maritimes chosen
    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.
  • B. Bouches-du-Rhône
    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.
  • C. Hautes-Alpes
    Hautes-Alpes is a mountainous department in southeastern France known for its Alpine landscapes, ski resorts, and outdoor recreation.
  • D. Drôme
    Drôme is a department in southeastern France known for its diverse landscapes, historic towns, and location between the Alps and the Rhône Valley.
  • E. Alpes-de-Haute-Provence
    Alpes-de-Haute-Provence is a mountainous department in southeastern France known for its Alpine landscapes, lavender fields, and picturesque Provençal villages.
  • 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_69ad85d1a3948190931fd1ea1f49717b completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbc9a14c881908932b17ed3eececb completed March 8, 2026, 6:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5335e1e348190912455cd90009558 completed March 14, 2026, 10:07 a.m.
Created at: March 8, 2026, 3:19 p.m.