Triple

T6304148
Position Surface form Disambiguated ID Type / Status
Subject Vestre Aker E141330 entity
Predicate contains P35 FINISHED
Object Røa
Røa is a residential neighborhood in the western part of Oslo, Norway, known for its green surroundings and good public transport connections.
E669588 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: Røa | Statement: [Vestre Aker, contains, Røa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Røa
Context triple: [Vestre Aker, contains, Røa]
  • A. Drammen
    Drammen is a city and municipality in southeastern Norway known for its riverside setting along the Drammenselva and its role as a regional commercial and transport hub.
  • B. Rodeløkka
    Rodeløkka is a historic residential neighborhood in Oslo, Norway, known for its wooden houses, narrow streets, and close-knit, village-like atmosphere within the inner city.
  • C. Røyken
    Røyken is a former municipality and suburban area in southeastern Norway, located along the Oslofjord and historically part of Buskerud county.
  • D. Bjerkreim
    Bjerkreim is a rural municipality in southwestern Norway known for its rivers, salmon fishing, and agricultural landscape.
  • E. Ullensaker
    Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
  • 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: Røa
Triple: [Vestre Aker, contains, Røa]
Generated description
Røa is a residential neighborhood in the western part of Oslo, Norway, known for its green surroundings and good public transport connections.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Røa
Target entity description: Røa is a residential neighborhood in the western part of Oslo, Norway, known for its green surroundings and good public transport connections.
  • A. Drammen
    Drammen is a city and municipality in southeastern Norway known for its riverside setting along the Drammenselva and its role as a regional commercial and transport hub.
  • B. Rodeløkka
    Rodeløkka is a historic residential neighborhood in Oslo, Norway, known for its wooden houses, narrow streets, and close-knit, village-like atmosphere within the inner city.
  • C. Røyken
    Røyken is a former municipality and suburban area in southeastern Norway, located along the Oslofjord and historically part of Buskerud county.
  • D. Bjerkreim
    Bjerkreim is a rural municipality in southwestern Norway known for its rivers, salmon fishing, and agricultural landscape.
  • E. Ullensaker
    Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
  • 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_69c008cf0ad4819095def81e2bd42f9f completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0645f26a881909d5746151c0843cc completed March 22, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69c845c232188190a3391ce5159f49bb completed March 28, 2026, 9:18 p.m.
NEDg Description generation batch_69c846d0b1348190bc2bf23e75c535a9 completed March 28, 2026, 9:23 p.m.
NED2 Entity disambiguation (via description) batch_69c84748b8988190b4f85c253ea9e403 completed March 28, 2026, 9:25 p.m.
Created at: March 22, 2026, 4:28 p.m.