Triple

T16424479
Position Surface form Disambiguated ID Type / Status
Subject Märkischer Kreis E398905 entity
Predicate hasMunicipality P847 FINISHED
Object Iserlohn 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: Iserlohn | Statement: [Märkischer Kreis, hasMunicipality, Iserlohn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Iserlohn
Context triple: [Märkischer Kreis, hasMunicipality, Iserlohn]
  • A. Iserlohn chosen
    Iserlohn is a city in the Märkischer Kreis district of North Rhine-Westphalia, Germany, known historically for its role in World War II and its metalworking and industrial heritage.
  • B. Nordhorn
    Nordhorn is a town in Lower Saxony, Germany, known as the administrative center of the Grafschaft Bentheim district near the Dutch border.
  • C. Detmold
    Detmold is a historic town in northwestern Germany that served as the capital and residence city of the former Principality of Lippe.
  • D. Meppen
    Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
  • E. Diepholz
    Diepholz is a town in Lower Saxony, Germany, known as a local administrative center and for its surrounding lake district and agricultural landscape.
  • 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_69d87f2b9024819085c20e52de95d583 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e328f9da9081908dadbdac4b2d38ec completed April 18, 2026, 6:47 a.m.
Created at: April 10, 2026, 5:09 a.m.