Triple

T6574530
Position Surface form Disambiguated ID Type / Status
Subject Elmshorn E155525 entity
Predicate locatedNear P294 FINISHED
Object Pinneberg E250427 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: Pinneberg | Statement: [Elmshorn, locatedNear, Pinneberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pinneberg
Context triple: [Elmshorn, locatedNear, Pinneberg]
  • A. Pinneberg chosen
    Pinneberg is a town in northern Germany that serves as the administrative center of the district of the same name near Hamburg.
  • B. Lüneburg
    Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
  • C. Papenburg
    Papenburg is a German town in Lower Saxony best known for its historic canals and its large Meyer Werft shipyard, one of the world’s leading builders of cruise ships.
  • D. Norderstedt
    Norderstedt is a city in northern Germany that forms part of the Hamburg metropolitan area and is one of the larger urban centers in the state of Schleswig-Holstein.
  • E. Northeim
    Northeim is a town in Lower Saxony, Germany, known for its medieval old town and location in the Leine River valley.
  • 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_69c688151254819080387f87deab8fa7 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ae7134708190a0355519d117ab9c completed March 27, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c70ae438b0819086449c169e5c7e49 completed March 27, 2026, 10:55 p.m.
Created at: March 27, 2026, 1:53 p.m.