Triple

T3241569
Position Surface form Disambiguated ID Type / Status
Subject Amager Island E67975 entity
Predicate contains P35 FINISHED
Object Ørestad 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 | Statement: [Amager Island, contains, Ørestad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ørestad
Context triple: [Amager Island, contains, Ørestad]
  • 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. 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.
  • C. Ballerup
    Ballerup is a suburban municipality near Copenhagen in eastern Denmark, known for its residential areas, business parks, and sports facilities.
  • D. Rødovre
    Rødovre is a suburban municipality in the Capital Region of Denmark, located just west of central Copenhagen.
  • E. Kastrup
    Kastrup is a district in the Tårnby Municipality near Copenhagen, Denmark, best known for hosting the country’s main international airport.
  • 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_69ad858d27348190abb61c280b4c86a9 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaef76d908190815bb456e366ee0a completed March 8, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b34bb1c468819083b50b5858f8afe0 completed March 12, 2026, 11:26 p.m.
Created at: March 8, 2026, 3:08 p.m.