Triple

T10797626
Position Surface form Disambiguated ID Type / Status
Subject Hamburg metropolitan region E254750 entity
Predicate containsUrbanArea P11388 FINISHED
Object Elmshorn E155525 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: Elmshorn | Statement: [Hamburg metropolitan region, containsUrbanArea, Elmshorn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elmshorn
Context triple: [Hamburg metropolitan region, containsUrbanArea, Elmshorn]
  • A. Elmshorn chosen
    Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
  • B. Aurich
    Aurich is a historic town in northwestern Germany that serves as one of the principal urban centers of the East Frisia region in Lower Saxony.
  • C. Groß Borstel
    Groß Borstel is a residential district of Hamburg, Germany, situated near Hamburg Airport and characterized by a mix of urban housing and green spaces.
  • D. Travemünde
    Travemünde is a Baltic Sea resort town and seaside district of Lübeck in northern Germany, known for its beaches, harbor, and maritime tourism.
  • E. Delmenhorst
    Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
  • 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_69d6aa61c15c8190a1839550c56e75e1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d73333dc4081909faa40c10bce2735 completed April 9, 2026, 5:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69de566352608190ab15e3a4b690c9a5 completed April 14, 2026, 2:59 p.m.
Created at: April 8, 2026, 9:17 p.m.