Triple

T6193653
Position Surface form Disambiguated ID Type / Status
Subject Kristiansand E138455 entity
Predicate hasDistrict P459 FINISHED
Object Lund
Lund is a district of the Norwegian city of Kristiansand, known for its residential areas, educational institutions, and proximity to the city center.
 E656221 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: Lund | Statement: [Kristiansand, hasDistrict, Lund]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lund
Context triple: [Kristiansand, hasDistrict, Lund]
  • A. Lund
    Lund is a common Scandinavian surname of Swedish origin.
  • B. Lund
    Lund is a historic city in southern Sweden known for its medieval cathedral, prestigious university, and role as a significant cultural and academic center in Scandinavia.
  • C. Sundsvall
    Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
  • D. Örebro
    Örebro is a historic city in central Sweden known for its medieval castle, university, and role as a regional economic and cultural hub.
  • E. Malmö
    Malmö is a major coastal city in southern Sweden known for its historic center, modern architecture like the Turning Torso, and its role as a cultural and economic hub connected to Copenhagen via the Öresund Bridge.
  • 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: Lund
Triple: [Kristiansand, hasDistrict, Lund]
Generated description
Lund is a district of the Norwegian city of Kristiansand, known for its residential areas, educational institutions, and proximity to the city center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lund
Target entity description: Lund is a district of the Norwegian city of Kristiansand, known for its residential areas, educational institutions, and proximity to the city center.
  • A. Lund
    Lund is a common Scandinavian surname of Swedish origin.
  • B. Lund
    Lund is a historic city in southern Sweden known for its medieval cathedral, prestigious university, and role as a significant cultural and academic center in Scandinavia.
  • C. Sundsvall
    Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
  • D. Örebro
    Örebro is a historic city in central Sweden known for its medieval castle, university, and role as a regional economic and cultural hub.
  • E. Malmö
    Malmö is a major coastal city in southern Sweden known for its historic center, modern architecture like the Turning Torso, and its role as a cultural and economic hub connected to Copenhagen via the Öresund Bridge.
  • 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_69c062431ae88190a1bf6ca91f3dc690 completed March 22, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7eeb127248190bbee7a9f69c2b980 completed March 28, 2026, 3:07 p.m.
NEDg Description generation batch_69c7ef372eb481909e551fc9f23a9d51 completed March 28, 2026, 3:09 p.m.
NED2 Entity disambiguation (via description) batch_69c7efbfc3e08190b06a012e4aba6b1b completed March 28, 2026, 3:11 p.m.
Created at: March 22, 2026, 4:19 p.m.