Triple

T11325676
Position Surface form Disambiguated ID Type / Status
Subject Uerdingen E268210 entity
Predicate locatedIn P40 FINISHED
Object Nordrhein-Westfalen E20221 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: Nordrhein-Westfalen | Statement: [Uerdingen, locatedIn, Nordrhein-Westfalen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nordrhein-Westfalen
Context triple: [Uerdingen, locatedIn, Nordrhein-Westfalen]
  • A. North Rhine-Westphalia chosen
    North Rhine-Westphalia is Germany’s most populous federal state, known for its major industrial regions, cultural hubs like Cologne and Düsseldorf, and numerous universities and research institutions.
  • B. Rhineland-Palatinate
    Rhineland-Palatinate is a federal state in western Germany known for its wine-growing regions along the Rhine and Moselle rivers and its historic cities such as Mainz and Trier.
  • C. South Westphalia
    South Westphalia is a region in western Germany known for its mixed industrial and rural character, encompassing parts of North Rhine-Westphalia including the Arnsberg area.
  • D. Hamburg state
    Hamburg state is a federal state of Germany that consists primarily of the city of Hamburg, a major northern European port and cultural center.
  • E. NRW
    NRW is the three-letter National Rail station code used to identify Norwich railway station in the UK rail network.
  • 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_69d6aacb1f0881908c84a349fd1be047 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9e2253881909518cad0f12ef612 completed April 9, 2026, 6:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5e8b19b1c8190bc9147a9fc73e35b completed April 20, 2026, 8:49 a.m.
Created at: April 8, 2026, 9:32 p.m.