Triple

T2438789
Position Surface form Disambiguated ID Type / Status
Subject Bad Honnef E53223 entity
Predicate hasTwinTown P919 FINISHED
Object Ludvika
Ludvika is a small industrial town in central Sweden known for its engineering and manufacturing industries, particularly in the power and electrical sectors.
 E266858 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: Ludvika | Statement: [Bad Honnef, hasTwinTown, Ludvika]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ludvika
Context triple: [Bad Honnef, hasTwinTown, Ludvika]
  • A. Vratislavia
    Vratislavia is the historical Latin name of the city now known as Wrocław in southwestern Poland.
  • B. Dagmar
    Dagmar is a feminine given name of Germanic origin, historically associated with European nobility and still used in various countries today.
  • C. Hedvig
    Hedvig is a Scandinavian female given name, historically borne by several notable women in Swedish and broader Nordic royalty and nobility.
  • D. Luitgard
    Luitgard was the fourth and last wife of Charlemagne, serving briefly as Frankish queen consort in the late 8th century.
  • E. Lopokova
    Lopokova is the surname of Lydia Lopokova, a renowned Russian ballerina associated with the Ballets Russes and later known for her marriage to economist John Maynard Keynes.
  • 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: Ludvika
Triple: [Bad Honnef, hasTwinTown, Ludvika]
Generated description
Ludvika is a small industrial town in central Sweden known for its engineering and manufacturing industries, particularly in the power and electrical sectors.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ludvika
Target entity description: Ludvika is a small industrial town in central Sweden known for its engineering and manufacturing industries, particularly in the power and electrical sectors.
  • A. Vratislavia
    Vratislavia is the historical Latin name of the city now known as Wrocław in southwestern Poland.
  • B. Dagmar
    Dagmar is a feminine given name of Germanic origin, historically associated with European nobility and still used in various countries today.
  • C. Hedvig
    Hedvig is a Scandinavian female given name, historically borne by several notable women in Swedish and broader Nordic royalty and nobility.
  • D. Luitgard
    Luitgard was the fourth and last wife of Charlemagne, serving briefly as Frankish queen consort in the late 8th century.
  • E. Lopokova
    Lopokova is the surname of Lydia Lopokova, a renowned Russian ballerina associated with the Ballets Russes and later known for her marriage to economist John Maynard Keynes.
  • 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_69ab495b6dac8190ac82661aa1452222 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc9f62ad081909373134c5adf65d9 completed March 7, 2026, 6:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69aef0b0e920819088bd7ee3684c81fe completed March 9, 2026, 4:09 p.m.
NEDg Description generation batch_69aef4d2cd3c81908773ac7ff9e4b28d completed March 9, 2026, 4:26 p.m.
NED2 Entity disambiguation (via description) batch_69aef54c3f1c819085019bd8e48d7db7 completed March 9, 2026, 4:29 p.m.
Created at: March 6, 2026, 9:43 p.m.