Triple

T23030043
Position Surface form Disambiguated ID Type / Status
Subject Homansbyen E573431 entity
Predicate hasStreet P959 FINISHED
Object Underhaugsveien
Underhaugsveien is a residential street in the central Oslo neighborhood of Homansbyen, known for its historic urban architecture and proximity to the city center.
 E1565938 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: Underhaugsveien | Statement: [Homansbyen, hasStreet, Underhaugsveien]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Underhaugsveien
Context triple: [Homansbyen, hasStreet, Underhaugsveien]
  • A. Dokkveien
    Dokkveien is a street located in the Vika neighborhood of central Oslo, Norway, near the city’s waterfront and business district.
  • B. Svenskens vei
    Svenskens vei is a street located in the Vålerenga neighborhood of Oslo, Norway.
  • C. Lysebotnvegen
    Lysebotnvegen is a scenic mountain road in Norway known for its dramatic hairpin bends, steep climbs, and panoramic views over the Lysefjord.
  • D. Hamngatan
    Hamngatan is a major shopping and traffic street in central Stockholm, Sweden, known for its department stores and proximity to Sergels torg.
  • E. Kirkegata
    Kirkegata is a central street in Oslo, Norway, known for its historic buildings and proximity to major city squares and landmarks.
  • 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: Underhaugsveien
Triple: [Homansbyen, hasStreet, Underhaugsveien]
Generated description
Underhaugsveien is a residential street in the central Oslo neighborhood of Homansbyen, known for its historic urban architecture and proximity to the city center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Underhaugsveien
Target entity description: Underhaugsveien is a residential street in the central Oslo neighborhood of Homansbyen, known for its historic urban architecture and proximity to the city center.
  • A. Dokkveien
    Dokkveien is a street located in the Vika neighborhood of central Oslo, Norway, near the city’s waterfront and business district.
  • B. Svenskens vei
    Svenskens vei is a street located in the Vålerenga neighborhood of Oslo, Norway.
  • C. Lysebotnvegen
    Lysebotnvegen is a scenic mountain road in Norway known for its dramatic hairpin bends, steep climbs, and panoramic views over the Lysefjord.
  • D. Hamngatan
    Hamngatan is a major shopping and traffic street in central Stockholm, Sweden, known for its department stores and proximity to Sergels torg.
  • E. Kirkegata
    Kirkegata is a central street in Oslo, Norway, known for its historic buildings and proximity to major city squares and landmarks.
  • 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_69e245b911188190bc3d96326c847969 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f184803e908190b1e58545b9c3d587 completed April 29, 2026, 4:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0bf2b3ac308190bc241acfd02903d8 completed May 19, 2026, 5:18 a.m.
NEDg Description generation batch_6a0bf70818608190b6d7c5ee633bb4ce completed May 19, 2026, 5:37 a.m.
NED2 Entity disambiguation (via description) batch_6a0bf76284988190ac4b8f08859aecc6 completed May 19, 2026, 5:38 a.m.
Created at: April 17, 2026, 3:53 p.m.