Triple

T16290625
Position Surface form Disambiguated ID Type / Status
Subject Lledoner Pelut E395510 entity
Predicate grownInRegion P2078 FINISHED
Object Roussillon E1204820 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: Roussillon | Statement: [Lledoner Pelut, grownInRegion, Roussillon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Roussillon
Context triple: [Lledoner Pelut, grownInRegion, Roussillon]
  • A. Roussillon chosen
    Roussillon is a historic province in southern France, bordering Spain and the Mediterranean, known for its Catalan heritage, vineyards, and coastal landscapes.
  • B. Tarragonès
    Tarragonès is a coastal comarca (county) in the province of Tarragona, Catalonia, Spain, known for its capital city Tarragona and its Mediterranean shoreline.
  • C. Occitania
    Occitania is a historical and cultural region in southern Europe, mainly in southern France and parts of Italy and Spain, traditionally associated with the Occitan language and a distinct Romance cultural heritage.
  • D. Languedoc
    Languedoc is a historic region in southern France known for its Occitan culture, medieval towns, and long-standing wine-making tradition.
  • E. Occitanie
    Occitanie is a large administrative region in southern France known for its Mediterranean coastline, historic cities like Toulouse and Montpellier, and diverse landscapes ranging from coastal plains to the Pyrenees.
  • 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_69d87f22c7248190a54c949738441e2e completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2491821d0819086cffdd7551ba85a completed April 17, 2026, 2:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00b276be5c8190a42ce541168ab7d0 completed May 10, 2026, 4:29 p.m.
Created at: April 10, 2026, 5:05 a.m.