Triple

T10229714
Position Surface form Disambiguated ID Type / Status
Subject Kronoberg County E243306 entity
Predicate contains P35 FINISHED
Object Ljungby
Ljungby is a small Swedish town in southern Småland known for its lakeside surroundings, forestry-based economy, and role as a local commercial and cultural center.
 E854805 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: Ljungby | Statement: [Kronoberg County, contains, Ljungby]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ljungby
Context triple: [Kronoberg County, contains, Ljungby]
  • A. Ronneby
    Ronneby is a historic town in southern Sweden known for its well-preserved wooden architecture, spa traditions, and scenic location in Blekinge County.
  • B. Sandviken
    Sandviken is an industrial town in central Sweden, best known as the historic home of the steel company Sandvik.
  • C. Hörby
    Hörby is a small municipality in southern Sweden’s Skåne County, known for its rural landscape and traditional Swedish town character.
  • D. Strängnäs
    Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
  • E. Hjulsta
    Hjulsta is a suburb in northwestern Stockholm, Sweden, known for being the terminus of one of the Stockholm metro lines.
  • 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: Ljungby
Triple: [Kronoberg County, contains, Ljungby]
Generated description
Ljungby is a small Swedish town in southern Småland known for its lakeside surroundings, forestry-based economy, and role as a local commercial and cultural center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ljungby
Target entity description: Ljungby is a small Swedish town in southern Småland known for its lakeside surroundings, forestry-based economy, and role as a local commercial and cultural center.
  • A. Ronneby
    Ronneby is a historic town in southern Sweden known for its well-preserved wooden architecture, spa traditions, and scenic location in Blekinge County.
  • B. Sandviken
    Sandviken is an industrial town in central Sweden, best known as the historic home of the steel company Sandvik.
  • C. Hörby
    Hörby is a small municipality in southern Sweden’s Skåne County, known for its rural landscape and traditional Swedish town character.
  • D. Strängnäs
    Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
  • E. Hjulsta
    Hjulsta is a suburb in northwestern Stockholm, Sweden, known for being the terminus of one of the Stockholm metro lines.
  • 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_69d381b0f97c819085c9b45799a5fb7c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d1fcb1d081908173033594a6bfc9 completed April 7, 2026, 9:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69d71c9123cc819095da6d8dc0cfa688 completed April 9, 2026, 3:27 a.m.
NEDg Description generation batch_69d73180d90481908f1b4768230edd36 completed April 9, 2026, 4:56 a.m.
NED2 Entity disambiguation (via description) batch_69d7326b14988190bff33dc01e690707 completed April 9, 2026, 5 a.m.
Created at: April 6, 2026, 11:19 a.m.