Triple

T9833927
Position Surface form Disambiguated ID Type / Status
Subject Marburg-Biedenkopf E239053 entity
Predicate bordersWith P224 FINISHED
Object Siegen-Wittgenstein E413003 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: Siegen-Wittgenstein | Statement: [Marburg-Biedenkopf, bordersWith, Siegen-Wittgenstein]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siegen-Wittgenstein
Context triple: [Marburg-Biedenkopf, bordersWith, Siegen-Wittgenstein]
  • A. Siegen-Wittgenstein chosen
    Siegen-Wittgenstein is a rural district in the German state of North Rhine-Westphalia, known for its forested low mountain landscapes and the city of Siegen as its administrative center.
  • B. Siegen
    Siegen is a city in western Germany known as the birthplace of the Baroque painter Peter Paul Rubens and for its historic mining and university traditions.
  • C. Solingen
    Solingen is a city in western Germany renowned for its centuries-old blade-making tradition and production of high-quality knives and swords.
  • D. Wehrheim
    Wehrheim is a small municipality in the Hochtaunus district of Hesse, Germany, known for its rural character and proximity to the Taunus mountain range.
  • E. Kaiserslautern
    Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
  • 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_69ca84e314108190978324a4bdb959f8 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb3385054819094145c96204e3f0d completed April 2, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69d71c3fa4b88190be4ce7b64335a99f completed April 9, 2026, 3:25 a.m.
Created at: March 30, 2026, 8:32 p.m.