Triple

T7196605
Position Surface form Disambiguated ID Type / Status
Subject Pico de Aneto E168630 entity
Predicate near P350 FINISHED
Object Benasque E282614 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: Benasque | Statement: [Pico de Aneto, near, Benasque]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Benasque
Context triple: [Pico de Aneto, near, Benasque]
  • A. Benasque Valley chosen
    Benasque Valley is a scenic glacial valley in the central Pyrenees of Spain, renowned for its high mountain landscapes, hiking and skiing, and proximity to the range’s highest peaks.
  • B. Pau-Ferro
    Pau-Ferro is a neighborhood in the city of Recife, Brazil.
  • C. Baqueira-Beret
    Baqueira-Beret is a major ski resort in the Spanish Pyrenees, renowned for its extensive slopes, reliable snow, and popularity among both domestic and international skiers.
  • D. Manresa
    Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
  • E. Garraf
    Garraf is a coastal comarca in Catalonia, Spain, known for its Mediterranean landscapes, natural park, and seaside towns such as Sitges and Vilanova i la Geltrú.
  • 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_69c68a5376748190bb500f03df86e93e completed March 27, 2026, 1:46 p.m.
NER Named-entity recognition batch_69c6e928ecdc8190a7f3feaf6d28781b completed March 27, 2026, 8:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7bfa14e1c8190968b207bef0c96a9 completed March 28, 2026, 11:46 a.m.
Created at: March 27, 2026, 2:51 p.m.