Triple

T8002696
Position Surface form Disambiguated ID Type / Status
Subject Bærum E186288 entity
Predicate hasSettlement P1068 FINISHED
Object Bekkestua E164673 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: Bekkestua | Statement: [Bærum, hasSettlement, Bekkestua]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bekkestua
Context triple: [Bærum, hasSettlement, Bekkestua]
  • A. Bekkestua chosen
    Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
  • B. Bålsta
    Bålsta is a locality in Uppsala County, Sweden, known as the main urban center of Håbo Municipality and a commuter town within the Greater Stockholm region.
  • C. Sørenga
    Sørenga is a modern waterfront neighborhood in Oslo, Norway, known for its residential developments, seaside promenade, and popular public seawater pool and beach.
  • D. Byrkjedal
    Byrkjedal is a small rural village in southwestern Norway, known for its scenic valley setting and traditional Norwegian countryside character.
  • E. Finnsnes
    Finnsnes is a small coastal town in northern Norway that serves as a commercial and transport hub for the island municipality of Senja.
  • 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_69ca82aaaf24819084b94d18f699ba53 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3cf2918081909ee0afab11caed63 completed March 31, 2026, 3:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc93a132c08190bc9a00667aa32b90 completed April 1, 2026, 3:40 a.m.
Created at: March 30, 2026, 5:18 p.m.