Triple

T3874794
Position Surface form Disambiguated ID Type / Status
Subject Arnsberg region E92473 entity
Predicate seatOfGovernment P761 FINISHED
Object Arnsberg E359488 NE FINISHED

How this triple was built (2 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: Arnsberg | Statement: [Arnsberg region, seatOfGovernment, Arnsberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arnsberg
Context triple: [Arnsberg region, seatOfGovernment, Arnsberg]
  • A. Arnsberg chosen
    Arnsberg is a historic town in the Sauerland region of North Rhine-Westphalia, Germany, known for its medieval old town and surrounding forested hills.
  • B. Meppen
    Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
  • C. Nienburg
    Nienburg is a historic town in Lower Saxony, Germany, known for its medieval architecture and scenic location along the Weser River.
  • D. Lemgo
    Lemgo is a historic town in the Lippe district of North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and Hanseatic heritage.
  • E. Bentheim
    Bentheim is a historical county in Lower Saxony, Germany, known for its Reformed Protestant heritage and the former County of Bentheim.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69aed967448c819086c4b358d37b25aa completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec59bea08190b1e193f34944a2ee completed March 9, 2026, 3:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51c87214881908e03f5c770c58713 completed March 14, 2026, 8:29 a.m.
Created at: March 9, 2026, 3:20 p.m.