Triple

T884889
Position Surface form Disambiguated ID Type / Status
Subject Newcastle upon Tyne E19107 entity
Predicate hasTwinTown P919 FINISHED
Object Gelsenkirchen E167466 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: Gelsenkirchen | Statement: [Newcastle upon Tyne, hasTwinTown, Gelsenkirchen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gelsenkirchen
Context triple: [Newcastle upon Tyne, hasTwinTown, Gelsenkirchen]
  • A. Gelsenkirchen chosen
    Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
  • B. Recklinghausen
    Recklinghausen is a city in the Ruhr area of North Rhine-Westphalia, western Germany, known historically for coal mining and its role as a regional administrative center.
  • C. Remscheid
    Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
  • D. Bochum
    Bochum is a major city in Germany’s Ruhr region known for its industrial heritage, cultural institutions, and large university.
  • E. Duisburg
    Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
  • 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_69a4939c32488190a7ccd41cf0abb22b completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ace5e15c81908cc2e648c9cd52f2 completed March 1, 2026, 9:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69afbba1649081909703163de2d018cd completed March 10, 2026, 6:35 a.m.
Created at: March 1, 2026, 7:39 p.m.