Triple

T14818732
Position Surface form Disambiguated ID Type / Status
Subject Ørestad E348386 entity
Predicate hasPart P35 FINISHED
Object Ørestad City E348386 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: Ørestad City | Statement: [Ørestad, hasPart, Ørestad City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ørestad City
Context triple: [Ørestad, hasPart, Ørestad City]
  • A. Ørestad chosen
    Ørestad is a modern, rapidly developing district on the island of Amager in Copenhagen, known for its contemporary architecture, mixed-use urban planning, and proximity to both the city center and Copenhagen Airport.
  • B. Valby
    Valby is a district in Copenhagen, Denmark, known as an important local transport and residential area within the city.
  • C. Nordhavn
    Nordhavn is a harbor-side district in Copenhagen, Denmark, known for its large-scale urban redevelopment into a modern, sustainable waterfront neighborhood.
  • D. Frederiksberg
    Frederiksberg is an affluent, centrally located municipality in Denmark that forms an enclave within the city of Copenhagen and is known for its parks, cultural institutions, and historic architecture.
  • E. Ballerup
    Ballerup is a suburban municipality near Copenhagen in eastern Denmark, known for its residential areas, business parks, and sports facilities.
  • 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_69d822eb8f588190bf53445e730a934f completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69decfe4cf38819090f25ef045351d5d completed April 14, 2026, 11:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff677b7be08190afc2767835836908 completed May 9, 2026, 4:57 p.m.
Created at: April 10, 2026, 1:50 a.m.