Triple

T21784039
Position Surface form Disambiguated ID Type / Status
Subject Majorstuen E537788 entity
Predicate hasMainStreet P461 FINISHED
Object Hegdehaugsveien 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: Hegdehaugsveien | Statement: [Majorstuen, hasMainStreet, Hegdehaugsveien]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hegdehaugsveien
Context triple: [Majorstuen, hasMainStreet, Hegdehaugsveien]
  • A. Lysebotnvegen
    Lysebotnvegen is a scenic mountain road in Norway known for its dramatic hairpin bends, steep climbs, and panoramic views over the Lysefjord.
  • B. Hedmarksgata
    Hedmarksgata is a street located in the Vålerenga neighborhood of Oslo, Norway.
  • C. Kirkeveien chosen
    Kirkeveien is a prominent thoroughfare in Oslo, Norway, known for running through the Majorstuen area and connecting several central neighborhoods and parks.
  • D. Munkedamsveien
    Munkedamsveien is a central street in Oslo, Norway, known for hosting major cultural venues and offices near the city’s waterfront.
  • E. Vålerenggata
    Vålerenggata is a street located in the Vålerenga neighborhood of Oslo, Norway, known for its traditional wooden houses and historic urban character.
  • 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_69e0c47198f881908cb0d237266c10e9 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f046303d54819096b3fab4ab5678e6 completed April 28, 2026, 5:31 a.m.
Created at: April 16, 2026, 6:52 p.m.