Triple

T19692640
Position Surface form Disambiguated ID Type / Status
Subject Lippe (region) E472873 entity
Predicate hasPart P35 FINISHED
Object Lemgo NE NERFINISHED

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: Lemgo | Statement: [Lippe (region), hasPart, Lemgo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lemgo
Context triple: [Lippe (region), hasPart, Lemgo]
  • A. Lemgo chosen
    Lemgo is a historic town in the Lippe district of North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and Hanseatic heritage.
  • 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. Lüdinghausen
    Lüdinghausen is a historic town in western Germany known for its medieval castles and picturesque setting in the Münsterland region.
  • D. Gummersbach
    Gummersbach is a town in North Rhine-Westphalia, Germany, known as a regional center in the Bergisches Land and a location for higher education and industry.
  • E. Lohmar
    Lohmar is a town in the Rhein-Sieg district of North Rhine-Westphalia, Germany, situated near Cologne and known for its green surroundings and residential character.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e515bef88190bc30781aea50537a completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e64210cddc8190836faa2996a44457 completed April 20, 2026, 3:11 p.m.
Created at: April 10, 2026, 1:46 p.m.