Triple

T13614390
Position Surface form Disambiguated ID Type / Status
Subject Evje og Hornnes E325273 entity
Predicate hasSettlement P1068 FINISHED
Object Evje E1051189 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: Evje | Statement: [Evje og Hornnes, hasSettlement, Evje]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Evje
Context triple: [Evje og Hornnes, hasSettlement, Evje]
  • A. Evje chosen
    Evje is a village in southern Norway that serves as a local commercial and service hub in the Setesdal valley.
  • B. Øyeren
    Øyeren is a large lake in southeastern Norway, known for its rich birdlife and role as a major reservoir along the Glomma river system.
  • C. Tveit
    Tveit is a Norwegian surname most notably borne by American actor and singer Aaron Tveit, known for his work in musical theatre, film, and television.
  • D. Lessebo
    Lessebo is a small locality and municipality in southern Sweden known for its traditional paper mill and glassmaking heritage.
  • E. Onsøy
    Onsøy is a former municipality and coastal district that now forms part of the city and municipality of Fredrikstad in Viken county, Norway.
  • 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_69d8076aae28819092cf636190ee5529 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb0abe1208190a1e0a32dc141d836 completed April 12, 2026, 2:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f78ae7d6908190836172e955b0d7e8 completed May 3, 2026, 5:50 p.m.
Created at: April 9, 2026, 9:50 p.m.