Triple

T10668481
Position Surface form Disambiguated ID Type / Status
Subject Åna-Sira fjord E251420 entity
Predicate locatedIn P40 FINISHED
Object Agder county E120897 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: Agder county | Statement: [Åna-Sira fjord, locatedIn, Agder county]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Agder county
Context triple: [Åna-Sira fjord, locatedIn, Agder county]
  • A. Agder chosen
    Agder is a county in southern Norway known for its long coastline, maritime heritage, and popular coastal towns and islands.
  • B. Aust-Agder
    Aust-Agder was a former county in southern Norway known for its coastal towns, forests, and role in the country’s maritime and timber industries.
  • C. Akershus county
    Akershus county was a former county in southeastern Norway that historically surrounded Oslo and included both urban suburbs and rural areas before being merged into Viken county.
  • D. Trøndelag County
    Trøndelag County is a large region in central Norway known for its historic city of Trondheim, coastal and fjord landscapes, and role as a cultural and economic hub of the country.
  • E. Telemark county
    Telemark county was a former county in southeastern Norway known for its rich folk culture, traditional architecture, and varied landscape of mountains, forests, and coastal areas.
  • 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_69d6aa5b0d2881909584b20efc5877f0 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6f860790c81909c2c1d3c489ec5b4 completed April 9, 2026, 12:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69e4cbce653481909b201a2d5871e129 completed April 19, 2026, 12:34 p.m.
Created at: April 8, 2026, 9:08 p.m.