Triple

T21890912
Position Surface form Disambiguated ID Type / Status
Subject Lyngdal E540541 entity
Predicate administrativeCentre P1474 FINISHED
Object Lyngdal town 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: Lyngdal town | Statement: [Lyngdal, administrativeCentre, Lyngdal town]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lyngdal town
Context triple: [Lyngdal, administrativeCentre, Lyngdal town]
  • A. Lyngdal chosen
    Lyngdal is a coastal town and municipality in southern Norway known for its beaches, fjords, and tourism.
  • B. Slemdal
    Slemdal is a residential neighborhood in the Vestre Aker borough of Oslo, Norway, known for its green surroundings and affluent character.
  • C. Ranheim
    Ranheim is a residential neighborhood and former industrial village in the city of Trondheim, Norway, located along the Trondheimsfjord.
  • D. Lørenskog
    Lørenskog is a suburban municipality in Viken county, Norway, located just east of Oslo and known for its residential areas and commercial centers.
  • E. Larvik
    Larvik is a coastal town and municipality in Vestfold, Norway, known for its harbor, beaches, and historic connections to the shipping and timber industries.
  • 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_69e0c47a95908190ae3e19b716accb3d completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f11fc2124c8190a79cf115a1d30283 completed April 28, 2026, 8:59 p.m.
Created at: April 16, 2026, 7:06 p.m.