Triple

T15423808
Position Surface form Disambiguated ID Type / Status
Subject Schwelm E369451 entity
Predicate hasTwinTown P919 FINISHED
Object Iserlohn E346114 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: Iserlohn | Statement: [Schwelm, hasTwinTown, Iserlohn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Iserlohn
Context triple: [Schwelm, hasTwinTown, 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 (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_69d85a1849f48190bf898068b2806fae completed April 10, 2026, 2:02 a.m.
NER Named-entity recognition batch_69e03ec032548190840b558dde6057c7 completed April 16, 2026, 1:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a009d232074819083f58de3ee5fbf7d completed May 10, 2026, 2:58 p.m.
Created at: April 10, 2026, 3:20 a.m.