Triple

T15755484
Position Surface form Disambiguated ID Type / Status
Subject Norwegian airspace E381956 entity
Predicate contains P35 FINISHED
Object Tromsø TMA
Tromsø TMA is a controlled terminal airspace sector in northern Norway that manages arriving and departing air traffic for the Tromsø area.
E1177727 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: Tromsø TMA | Statement: [Norwegian airspace, contains, Tromsø TMA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tromsø TMA
Context triple: [Norwegian airspace, contains, Tromsø TMA]
  • A. Trondheim TMA
    Trondheim TMA is a controlled terminal airspace sector surrounding Trondheim Airport in Norway, managing arriving and departing air traffic in the region.
  • B. Tromsø
    Tromsø is a city in northern Norway known for its Arctic location, vibrant cultural scene, and prominence as a viewing spot for the Northern Lights.
  • C. Torshov
    Torshov is a residential neighborhood in Oslo, Norway, known for its early 20th-century architecture, green spaces, and vibrant local culture.
  • D. Sogndal
    Sogndal is a village and municipality in Vestland county, Norway, known for its scenic fjord landscape, agriculture, and as a regional education and service center.
  • E. Smestad
    Smestad is a residential neighborhood in Oslo, Norway, known for its affluent housing and proximity to green areas and good public transport.
  • 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: Tromsø TMA
Triple: [Norwegian airspace, contains, Tromsø TMA]
Generated description
Tromsø TMA is a controlled terminal airspace sector in northern Norway that manages arriving and departing air traffic for the Tromsø area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tromsø TMA
Target entity description: Tromsø TMA is a controlled terminal airspace sector in northern Norway that manages arriving and departing air traffic for the Tromsø area.
  • A. Trondheim TMA
    Trondheim TMA is a controlled terminal airspace sector surrounding Trondheim Airport in Norway, managing arriving and departing air traffic in the region.
  • B. Tromsø
    Tromsø is a city in northern Norway known for its Arctic location, vibrant cultural scene, and prominence as a viewing spot for the Northern Lights.
  • C. Torshov
    Torshov is a residential neighborhood in Oslo, Norway, known for its early 20th-century architecture, green spaces, and vibrant local culture.
  • D. Sogndal
    Sogndal is a village and municipality in Vestland county, Norway, known for its scenic fjord landscape, agriculture, and as a regional education and service center.
  • E. Smestad
    Smestad is a residential neighborhood in Oslo, Norway, known for its affluent housing and proximity to green areas and good public transport.
  • 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_69d86d9e6b44819085d1f6a969ecb74c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e050b1ff4881909d5240d1d30f5c8b completed April 16, 2026, 3 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff9096d65c81908755cae83cc48e61 completed May 9, 2026, 7:52 p.m.
NEDg Description generation batch_69ff929442748190a8c5df31e6730046 completed May 9, 2026, 8:01 p.m.
NED2 Entity disambiguation (via description) batch_69ff935e5d3881908215b6f45b7b6bf5 completed May 9, 2026, 8:04 p.m.
Created at: April 10, 2026, 4:47 a.m.