Triple

T22478528
Position Surface form Disambiguated ID Type / Status
Subject Lillestrøm municipality E555698 entity
Predicate hasUrbanArea P316 FINISHED
Object Skedsmokorset E182120 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: Skedsmokorset | Statement: [Lillestrøm municipality, hasUrbanArea, Skedsmokorset]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Skedsmokorset
Context triple: [Lillestrøm municipality, hasUrbanArea, Skedsmokorset]
  • A. Skedsmo chosen
    Skedsmo is a former municipality in Viken county, Norway, located northeast of Oslo and known for its suburban communities and historical ties to the Oslo region.
  • B. Skjelten
    Skjelten is a small settlement in the municipality of Ørskog in Møre og Romsdal county, Norway.
  • C. Hjelset
    Hjelset is a village in Møre og Romsdal county, Norway, situated within Molde Municipality along the Romsdalsfjorden.
  • D. Bekkelaget
    Bekkelaget is a coastal residential neighborhood in Oslo, Norway, known for its hillside views over the Oslofjord and its traditional wooden houses.
  • E. Storslett
    Storslett is a small village and administrative center in Nordreisa Municipality in Troms og Finnmark county in northern 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_69e11e52c2048190952dc5df209b9bed completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15be653bc8190a2e5c47e38228bfe completed April 29, 2026, 1:16 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0b125fc0a881908c72c3d711604997 completed May 18, 2026, 1:21 p.m.
Created at: April 16, 2026, 8:49 p.m.