Triple

T6196054
Position Surface form Disambiguated ID Type / Status
Subject Østensjø E138510 entity
Predicate hasNeighbourhood P4813 FINISHED
Object Skøyenåsen
Skøyenåsen is a residential neighborhood in Oslo, Norway, known for its green surroundings and access to public transportation.
 E586972 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: Skøyenåsen | Statement: [Østensjø, hasNeighbourhood, Skøyenåsen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Skøyenåsen
Context triple: [Østensjø, hasNeighbourhood, Skøyenåsen]
  • A. Skøyen
    Skøyen is a neighborhood in western Oslo, Norway, known as a busy residential and commercial hub with strong public transport connections.
  • B. Skogsvåg
    Skogsvåg is a small coastal village in western Norway, located on the island of Sotra in Vestland county.
  • C. Jørstadmoen
    Jørstadmoen is a military base and village area in Lillehammer, Norway, known primarily as a key site for the Norwegian Armed Forces and home to important defense and cyber units.
  • D. Lysthaugen
    Lysthaugen is a small settlement located in the municipality of Verdal in Trøndelag county, Norway.
  • E. Mortensrud
    Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
  • 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: Skøyenåsen
Triple: [Østensjø, hasNeighbourhood, Skøyenåsen]
Generated description
Skøyenåsen is a residential neighborhood in Oslo, Norway, known for its green surroundings and access to public transportation.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Skøyenåsen
Target entity description: Skøyenåsen is a residential neighborhood in Oslo, Norway, known for its green surroundings and access to public transportation.
  • A. Skøyen
    Skøyen is a neighborhood in western Oslo, Norway, known as a busy residential and commercial hub with strong public transport connections.
  • B. Skogsvåg
    Skogsvåg is a small coastal village in western Norway, located on the island of Sotra in Vestland county.
  • C. Jørstadmoen
    Jørstadmoen is a military base and village area in Lillehammer, Norway, known primarily as a key site for the Norwegian Armed Forces and home to important defense and cyber units.
  • D. Lysthaugen
    Lysthaugen is a small settlement located in the municipality of Verdal in Trøndelag county, Norway.
  • E. Mortensrud
    Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
  • 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_69c008ab9b3081908a11b2c744838435 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0624571508190bd273b4a051fbe41 completed March 22, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c603e1381481908da3af3924e15e26 completed March 27, 2026, 4:13 a.m.
NEDg Description generation batch_69c606a905c48190888d54be27199110 completed March 27, 2026, 4:25 a.m.
NED2 Entity disambiguation (via description) batch_69c606f5b1d8819081167c5f8febb47e completed March 27, 2026, 4:26 a.m.
Created at: March 22, 2026, 4:20 p.m.