Triple

T6331633
Position Surface form Disambiguated ID Type / Status
Subject Nordre Aker E142393 entity
Predicate contains P35 FINISHED
Object Tåsen
Tåsen is a residential neighborhood in Oslo, Norway, known for its quiet streets, green spaces, and proximity to the city center.
 E585747 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: Tåsen | Statement: [Nordre Aker, contains, Tåsen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tåsen
Context triple: [Nordre Aker, contains, Tåsen]
  • A. Tantolunden
    Tantolunden is a large park and recreational area in Stockholm known for its allotment gardens, waterfront, and outdoor activities.
  • B. Tysvær
    Tysvær is a coastal municipality in southwestern Norway known for its fjords, islands, and location between the cities of Haugesund and Stavanger.
  • C. Tengbom
    Tengbom is a prominent Swedish architectural firm known for its influential role in shaping modern Scandinavian architecture.
  • D. Taalunie
    Taalunie is an international institution that coordinates and promotes the Dutch language and literature across Dutch-speaking regions.
  • E. Storslett
    Storslett is a small village and administrative center in Nordreisa Municipality in Troms og Finnmark county in northern Norway.
  • 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: Tåsen
Triple: [Nordre Aker, contains, Tåsen]
Generated description
Tåsen is a residential neighborhood in Oslo, Norway, known for its quiet streets, green spaces, and proximity to the city center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tåsen
Target entity description: Tåsen is a residential neighborhood in Oslo, Norway, known for its quiet streets, green spaces, and proximity to the city center.
  • A. Tantolunden
    Tantolunden is a large park and recreational area in Stockholm known for its allotment gardens, waterfront, and outdoor activities.
  • B. Tysvær
    Tysvær is a coastal municipality in southwestern Norway known for its fjords, islands, and location between the cities of Haugesund and Stavanger.
  • C. Tengbom
    Tengbom is a prominent Swedish architectural firm known for its influential role in shaping modern Scandinavian architecture.
  • D. Taalunie
    Taalunie is an international institution that coordinates and promotes the Dutch language and literature across Dutch-speaking regions.
  • E. Storslett
    Storslett is a small village and administrative center in Nordreisa Municipality in Troms og Finnmark county in northern Norway.
  • 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_69c008d4d8e88190ad301c05b08722ac completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c06514cbe8819096dbeb17ccb3e3d5 completed March 22, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6041f713c8190b27ba54181049377 completed March 27, 2026, 4:14 a.m.
NEDg Description generation batch_69c604d3839081909f98c37f0fe8f0af completed March 27, 2026, 4:17 a.m.
NED2 Entity disambiguation (via description) batch_69c6054d2e388190a4bafffce879b039 completed March 27, 2026, 4:19 a.m.
Created at: March 22, 2026, 4:30 p.m.