Triple

T23083042
Position Surface form Disambiguated ID Type / Status
Subject Bogstadveien E575527 entity
Predicate connectsArea P2564 FINISHED
Object Hegdehaugen
Hegdehaugen is a neighborhood in Oslo, Norway, known for its central location and proximity to the busy shopping street Bogstadveien.
 E1582436 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: Hegdehaugen | Statement: [Bogstadveien, connectsArea, Hegdehaugen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hegdehaugen
Context triple: [Bogstadveien, connectsArea, Hegdehaugen]
  • A. Maihaugen
    Maihaugen is a large open-air museum in Lillehammer, Norway, showcasing historic buildings and cultural history from the Gudbrandsdalen region and Norwegian life over several centuries.
  • B. Oseberghaugen
    Oseberghaugen is the burial mound in Norway that contained the famous Viking Age Oseberg ship and its rich grave goods.
  • C. Høybakken
    Høybakken is a small coastal village in Trøndelag county, Norway, situated within the municipality of Ørland.
  • D. Haugenstua
    Haugenstua is a residential neighborhood in the Groruddalen valley in Oslo, Norway, known for its apartment blocks, diverse population, and proximity to public transport.
  • E. Tonsenhagen
    Tonsenhagen is a residential neighborhood in Oslo, Norway, known for its post-war apartment blocks, green spaces, and proximity to the Lillomarka forest.
  • 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: Hegdehaugen
Triple: [Bogstadveien, connectsArea, Hegdehaugen]
Generated description
Hegdehaugen is a neighborhood in Oslo, Norway, known for its central location and proximity to the busy shopping street Bogstadveien.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hegdehaugen
Target entity description: Hegdehaugen is a neighborhood in Oslo, Norway, known for its central location and proximity to the busy shopping street Bogstadveien.
  • A. Maihaugen
    Maihaugen is a large open-air museum in Lillehammer, Norway, showcasing historic buildings and cultural history from the Gudbrandsdalen region and Norwegian life over several centuries.
  • B. Oseberghaugen
    Oseberghaugen is the burial mound in Norway that contained the famous Viking Age Oseberg ship and its rich grave goods.
  • C. Høybakken
    Høybakken is a small coastal village in Trøndelag county, Norway, situated within the municipality of Ørland.
  • D. Haugenstua
    Haugenstua is a residential neighborhood in the Groruddalen valley in Oslo, Norway, known for its apartment blocks, diverse population, and proximity to public transport.
  • E. Tonsenhagen
    Tonsenhagen is a residential neighborhood in Oslo, Norway, known for its post-war apartment blocks, green spaces, and proximity to the Lillomarka forest.
  • 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_69e245bf3e3c819086d3448720efc01b completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f18da304548190ab7a421c1ded0eb6 completed April 29, 2026, 4:48 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0c5dac48dc8190b985ce6b1730fe63 completed May 19, 2026, 12:55 p.m.
NEDg Description generation batch_6a0c5edefb448190b2ee53becb7bcb8d completed May 19, 2026, 1 p.m.
NED2 Entity disambiguation (via description) batch_6a0c5f58dd448190bdf6674d65098c3f completed May 19, 2026, 1:02 p.m.
Created at: April 17, 2026, 3:56 p.m.