Triple

T11372783
Position Surface form Disambiguated ID Type / Status
Subject Provence E269383 entity
Predicate historicallyIncludes P915 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: [Provence, historicallyIncludes, Alpes-Maritimes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alpes-Maritimes
Context triple: [Provence, historicallyIncludes, 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_69d6aacca1048190b39dbbc2174616fa completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7ea8d244c8190b865260338edb532 completed April 9, 2026, 6:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69e55649a9188190911608fef5894bd8 completed April 19, 2026, 10:25 p.m.
Created at: April 8, 2026, 9:33 p.m.