Triple

T591272
Position Surface form Disambiguated ID Type / Status
Subject Western Norway E17275 entity
Predicate containsCity P294 FINISHED
Object Bergen
Bergen is Norway's second-largest city, renowned for its historic harbor, surrounding mountains and fjords, and role as a former Hanseatic trading hub.
 E74082 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: Bergen | Statement: [Western Norway, containsCity, Bergen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bergen
Context triple: [Western Norway, containsCity, Bergen]
  • A. Oslo
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • 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. Fredrikstad
    Fredrikstad is a coastal city in southeastern Norway known for its well-preserved fortified old town and role as a regional educational and commercial center.
  • D. Sarpsborg
    Sarpsborg is a historic city and municipality in Viken county, Norway, known as one of the country’s oldest towns and an important industrial and administrative center in the Østfold region.
  • E. Lillehammer
    Lillehammer is a Norwegian town in the Gudbrandsdalen valley, best known internationally for staging the 1994 Winter Olympics.
  • 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: Bergen
Triple: [Western Norway, containsCity, Bergen]
Generated description
Bergen is Norway's second-largest city, renowned for its historic harbor, surrounding mountains and fjords, and role as a former Hanseatic trading hub.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bergen
Target entity description: Bergen is Norway's second-largest city, renowned for its historic harbor, surrounding mountains and fjords, and role as a former Hanseatic trading hub.
  • A. Oslo
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • 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. Fredrikstad
    Fredrikstad is a coastal city in southeastern Norway known for its well-preserved fortified old town and role as a regional educational and commercial center.
  • D. Sarpsborg
    Sarpsborg is a historic city and municipality in Viken county, Norway, known as one of the country’s oldest towns and an important industrial and administrative center in the Østfold region.
  • E. Lillehammer
    Lillehammer is a Norwegian town in the Gudbrandsdalen valley, best known internationally for staging the 1994 Winter Olympics.
  • 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_69a49379d09c8190ac7e00b24e2810b1 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49bbaf53081908eed240bed09f63b completed March 1, 2026, 8:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69a51554857481909c684b86b51aa126 completed March 2, 2026, 4:43 a.m.
NEDg Description generation batch_69a5163b240881909672bf4bc2ebe9cf completed March 2, 2026, 4:46 a.m.
NED2 Entity disambiguation (via description) batch_69a516a1e6508190a7ecb801f5080ddd completed March 2, 2026, 4:48 a.m.
Created at: March 1, 2026, 7:33 p.m.