Triple

T7853803
Position Surface form Disambiguated ID Type / Status
Subject Skedsmo E182120 entity
Predicate borderedBy P224 FINISHED
Object Lørenskog E293027 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: Lørenskog | Statement: [Skedsmo, borderedBy, Lørenskog]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lørenskog
Context triple: [Skedsmo, borderedBy, Lørenskog]
  • A. Lørenskog chosen
    Lørenskog is a suburban municipality in Viken county, Norway, located just east of Oslo and known for its residential areas and commercial centers.
  • B. Slemdal
    Slemdal is a residential neighborhood in the Vestre Aker borough of Oslo, Norway, known for its green surroundings and affluent character.
  • C. Lysaker
    Lysaker is a key transport and business hub in the western part of the Oslo metropolitan area in Norway, featuring a major railway and commuter center.
  • D. Torshov
    Torshov is a residential neighborhood in Oslo, Norway, known for its early 20th-century architecture, green spaces, and vibrant local culture.
  • E. Steinkjer
    Steinkjer is a town and municipality in central Norway that serves as an important regional center and administrative hub in Trøndelag county.
  • 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_69ca82869ee08190b8f9040dbc2c0467 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb18ed56d481909266d862e0ae152d completed March 31, 2026, 12:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5b1e9e808190a0eb2dea5288e743 completed March 31, 2026, 5:26 a.m.
Created at: March 30, 2026, 4:51 p.m.