Triple

T217666
Position Surface form Disambiguated ID Type / Status
Subject Salford E4141 entity
Predicate hasTwinTown P919 FINISHED
Object Lünen
Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
 E67615 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: Lünen | Statement: [Salford, hasTwinTown, Lünen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lünen
Context triple: [Salford, hasTwinTown, Lünen]
  • A. Kaiserslautern
    Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
  • B. Herrlingen
    Herrlingen is a small village in the German state of Baden-Württemberg, historically noted as the place where Field Marshal Erwin Rommel spent his final days during World War II.
  • C. Osnabrück
    Osnabrück is a historic city in Lower Saxony, Germany, known for its medieval architecture and role in the Peace of Westphalia.
  • D. Karlsruhe
    Karlsruhe is a major city in southwestern Germany best known as the seat of the country’s highest courts and a central hub of German constitutional jurisprudence.
  • E. Duisburg
    Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
  • 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: Lünen
Triple: [Salford, hasTwinTown, Lünen]
Generated description
Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lünen
Target entity description: Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
  • A. Kaiserslautern
    Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
  • B. Herrlingen
    Herrlingen is a small village in the German state of Baden-Württemberg, historically noted as the place where Field Marshal Erwin Rommel spent his final days during World War II.
  • C. Osnabrück
    Osnabrück is a historic city in Lower Saxony, Germany, known for its medieval architecture and role in the Peace of Westphalia.
  • D. Karlsruhe
    Karlsruhe is a major city in southwestern Germany best known as the seat of the country’s highest courts and a central hub of German constitutional jurisprudence.
  • E. Duisburg
    Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
  • 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_69a2573508588190b522c2476d91acfe completed Feb. 28, 2026, 2:47 a.m.
NER Named-entity recognition batch_69a25c5062e48190833be10e4770e1e9 completed Feb. 28, 2026, 3:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69a4cc562bc08190b5f1a6f143bd13d1 completed March 1, 2026, 11:31 p.m.
NEDg Description generation batch_69a4cd4fdd048190a0717e884a2a0d6d completed March 1, 2026, 11:35 p.m.
NED2 Entity disambiguation (via description) batch_69a4cdb9bc44819086cff5102a0ce7fd completed March 1, 2026, 11:37 p.m.
Created at: Feb. 28, 2026, 2:53 a.m.