Triple

T18126871
Position Surface form Disambiguated ID Type / Status
Subject Montana E433898 entity
Predicate locatedNear P294 FINISHED
Object Sierre 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: Sierre | Statement: [Montana, locatedNear, Sierre]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sierre
Context triple: [Montana, locatedNear, Sierre]
  • A. Sierre chosen
    Sierre is a municipality and important regional center in the canton of Valais in southwestern Switzerland, known for its wine production and bilingual French-German culture.
  • B. Alpu
    Alpu is a town and district in northwestern Turkey known for its agricultural activities and location within Eskişehir Province.
  • C. Sumapaz Massif
    The Sumapaz Massif is a high Andean region in central Colombia best known for containing Sumapaz Páramo, the largest páramo (high-altitude tropical moorland) ecosystem in the world.
  • D. Monchique
    Monchique is a mountainous spa town in southern Portugal known for its lush forests, thermal springs, and panoramic views over the Algarve region.
  • E. Beni Snassen Mountains
    The Beni Snassen Mountains are a rugged mountain range in northeastern Morocco known for their distinctive limestone formations, biodiversity, and cultural significance to local Amazigh communities.
  • 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_69d8b909e8cc81908df4cc2b8ea6d11f completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4ddef4cd88190b16ef0d6ed3968c6 completed April 19, 2026, 1:51 p.m.
Created at: April 10, 2026, 10:29 a.m.