Triple

T16196615
Position Surface form Disambiguated ID Type / Status
Subject Lysebotn E393076 entity
Predicate hasRoadConnection P385 FINISHED
Object Lysebotnvegen
Lysebotnvegen is a scenic mountain road in Norway known for its dramatic hairpin bends, steep climbs, and panoramic views over the Lysefjord.
 E1198740 NE FINISHED

How this triple was built (4 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: Lysebotnvegen | Statement: [Lysebotn, hasRoadConnection, Lysebotnvegen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lysebotnvegen
Context triple: [Lysebotn, hasRoadConnection, Lysebotnvegen]
  • A. Vålerenggata
    Vålerenggata is a street located in the Vålerenga neighborhood of Oslo, Norway, known for its traditional wooden houses and historic urban character.
  • B. Hedmarksgata
    Hedmarksgata is a street located in the Vålerenga neighborhood of Oslo, Norway.
  • C. Møllergata
    Møllergata is a central street in Oslo, Norway, known for its historic buildings and proximity to key political and commercial areas.
  • D. Bogstadveien
    Bogstadveien is a prominent shopping and commercial street in Oslo, Norway, known for its boutiques, cafes, and central location.
  • E. Markveien
    Markveien is a well-known street in the Grünerløkka district of Oslo, Norway, noted for its vibrant mix of shops, cafés, and urban culture.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lysebotnvegen
Triple: [Lysebotn, hasRoadConnection, Lysebotnvegen]
Generated description
Lysebotnvegen is a scenic mountain road in Norway known for its dramatic hairpin bends, steep climbs, and panoramic views over the Lysefjord.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lysebotnvegen
Target entity description: Lysebotnvegen is a scenic mountain road in Norway known for its dramatic hairpin bends, steep climbs, and panoramic views over the Lysefjord.
  • A. Vålerenggata
    Vålerenggata is a street located in the Vålerenga neighborhood of Oslo, Norway, known for its traditional wooden houses and historic urban character.
  • B. Hedmarksgata
    Hedmarksgata is a street located in the Vålerenga neighborhood of Oslo, Norway.
  • C. Møllergata
    Møllergata is a central street in Oslo, Norway, known for its historic buildings and proximity to key political and commercial areas.
  • D. Bogstadveien
    Bogstadveien is a prominent shopping and commercial street in Oslo, Norway, known for its boutiques, cafes, and central location.
  • E. Markveien
    Markveien is a well-known street in the Grünerløkka district of Oslo, Norway, noted for its vibrant mix of shops, cafés, and urban culture.
  • F. None of above. chosen

Provenance (5 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_69d87f1e49ac8190a311b54d32990576 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e222dace848190b1a98e47333b922b completed April 17, 2026, 12:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffff0f352081908324783743e47029 completed May 10, 2026, 3:44 a.m.
NEDg Description generation batch_6a00003d347481908285b2253fd20ac1 completed May 10, 2026, 3:49 a.m.
NED2 Entity disambiguation (via description) batch_6a0000adc1b08190abbafcabb4ebc079 completed May 10, 2026, 3:51 a.m.
Created at: April 10, 2026, 5:02 a.m.