Triple

T15427332
Position Surface form Disambiguated ID Type / Status
Subject Horsens E369544 entity
Predicate hasTwinTown P919 FINISHED
Object Hässleholm
Hässleholm is a town in southern Sweden’s Skåne County known as a regional railway hub and service center.
 E1235851 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: Hässleholm | Statement: [Horsens, hasTwinTown, Hässleholm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hässleholm
Context triple: [Horsens, hasTwinTown, Hässleholm]
  • A. Söderhamn
    Söderhamn is a coastal town in east-central Sweden known for its historical wooden architecture and role as the administrative and commercial center of the surrounding region.
  • B. Hammarö
    Hammarö is a Swedish island and municipality in Värmland County, known for its forests, coastline, and proximity to the city of Karlstad.
  • C. Lysekil
    Lysekil is a coastal town in western Sweden known for its picturesque archipelago, fishing heritage, and popular seaside tourism.
  • D. Hudiksvall
    Hudiksvall is a coastal town in east-central Sweden known for its historic wooden buildings and harbor on the Gulf of Bothnia.
  • E. Härnösand
    Härnösand is a coastal city in northern Sweden known for its historic architecture, maritime heritage, and role as an administrative and cultural center in the region.
  • 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: Hässleholm
Triple: [Horsens, hasTwinTown, Hässleholm]
Generated description
Hässleholm is a town in southern Sweden’s Skåne County known as a regional railway hub and service center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hässleholm
Target entity description: Hässleholm is a town in southern Sweden’s Skåne County known as a regional railway hub and service center.
  • A. Söderhamn
    Söderhamn is a coastal town in east-central Sweden known for its historical wooden architecture and role as the administrative and commercial center of the surrounding region.
  • B. Hammarö
    Hammarö is a Swedish island and municipality in Värmland County, known for its forests, coastline, and proximity to the city of Karlstad.
  • C. Lysekil
    Lysekil is a coastal town in western Sweden known for its picturesque archipelago, fishing heritage, and popular seaside tourism.
  • D. Hudiksvall
    Hudiksvall is a coastal town in east-central Sweden known for its historic wooden buildings and harbor on the Gulf of Bothnia.
  • E. Härnösand
    Härnösand is a coastal city in northern Sweden known for its historic architecture, maritime heritage, and role as an administrative and cultural center in the region.
  • 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_69d85a1849f48190bf898068b2806fae completed April 10, 2026, 2:02 a.m.
NER Named-entity recognition batch_69e03ec31f4881908b26ff7c381d7bc9 completed April 16, 2026, 1:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00b272ea6c8190b22fd78081446701 completed May 10, 2026, 4:29 p.m.
NEDg Description generation batch_6a00b385d9ec81908aa44d64a8da53d0 completed May 10, 2026, 4:34 p.m.
NED2 Entity disambiguation (via description) batch_6a00b82b89c0819085fe73fda0d98654 completed May 10, 2026, 4:54 p.m.
Created at: April 10, 2026, 3:20 a.m.