Triple

T7541521
Position Surface form Disambiguated ID Type / Status
Subject Gästrikland E178285 entity
Predicate borders P224 FINISHED
Object Dalarna E72579 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: Dalarna | Statement: [Gästrikland, borders, Dalarna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dalarna
Context triple: [Gästrikland, borders, Dalarna]
  • A. Dalarna chosen
    Dalarna is a historical province in central Sweden known for its distinct cultural traditions, including unique dialects, folk costumes, and the iconic Dala horse.
  • B. Bohuslän
    Bohuslän is a coastal province in western Sweden known for its rugged granite shoreline, fishing villages, and archipelago along the Skagerrak.
  • C. Ångermanland
    Ångermanland is a historical province in northern Sweden known for its deep river valleys, forested landscapes, and coastal areas along the Gulf of Bothnia.
  • D. Dalsland
    Dalsland is a historical province in western Sweden known for its forests, lakes, and rural landscapes.
  • E. Jämtland region
    Jämtland region is a sparsely populated county in central Sweden known for its lakes, forests, mountains, and outdoor recreation tourism.
  • 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_69c69f2be3888190a6667a27f8f195e9 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f8750f80819088ddfb7a5580b5df completed March 27, 2026, 9:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8d69516a88190912a9574aef3d1f8 completed March 29, 2026, 7:36 a.m.
Created at: March 27, 2026, 3:48 p.m.