Triple

T17525049
Position Surface form Disambiguated ID Type / Status
Subject Kingdom of Arles E426771 entity
Predicate includedRegion P285 FINISHED
Object Provence 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: Provence | Statement: [Kingdom of Arles, includedRegion, Provence]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Provence
Context triple: [Kingdom of Arles, includedRegion, Provence]
  • A. Provence chosen
    Provence is a historic region in southeastern France known for its picturesque lavender fields, Mediterranean coastline, and rich cultural and culinary traditions.
  • B. Languedoc
    Languedoc is a historic region in southern France known for its Occitan culture, medieval towns, and long-standing wine-making tradition.
  • C. Provence-Alpes-Côte d’Azur
    Provence-Alpes-Côte d’Azur is a region in southeastern France known for its Mediterranean coastline, picturesque villages, and cultural hubs such as Marseille and Nice.
  • D. Coteaux Varois en Provence
    Coteaux Varois en Provence is a French appellation in central Provence known for its high-altitude vineyards producing fresh, aromatic rosé, red, and white wines.
  • E. Languedoc-Roussillon
    Languedoc-Roussillon was a former administrative region in southern France, stretching from the Pyrenees to the Mediterranean and known for its historic cities, vineyards, and diverse landscapes.
  • 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_69d889de677081909b22d2657b1f0292 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e452d592a081909bf876d606158b2d completed April 19, 2026, 3:58 a.m.
Created at: April 10, 2026, 5:49 a.m.