Triple

T14016404
Position Surface form Disambiguated ID Type / Status
Subject Terminal 2 (Copenhagen Airport) E337214 entity
Predicate locatedIn P40 FINISHED
Object Kastrup E343798 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: Kastrup | Statement: [Terminal 2 (Copenhagen Airport), locatedIn, Kastrup]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kastrup
Context triple: [Terminal 2 (Copenhagen Airport), locatedIn, Kastrup]
  • A. Kastrup chosen
    Kastrup is a district in the Tårnby Municipality near Copenhagen, Denmark, best known for hosting the country’s main international airport.
  • B. Glostrup
    Glostrup is a suburban town and municipality in the Copenhagen metropolitan area of Denmark, known for its residential neighborhoods and commercial districts.
  • C. Hellerup
    Hellerup is a suburban district just north of central Copenhagen, known for its affluent residential areas, seaside location, and role as a key transport and commercial hub.
  • D. Emdrup
    Emdrup is a district in Copenhagen, Denmark, known for hosting a campus of Aarhus University and various educational and residential facilities.
  • E. Hornbæk
    Hornbæk is a coastal town in northern Zealand, Denmark, known for its sandy beaches, holiday villas, and role as a popular seaside resort.
  • 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_69d81c6543a48190bd5ba93d7419e797 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2f396b648190927e5718c3bb6511 completed April 14, 2026, 12:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcb653fedc81908cdd0dde2d3f3329 completed May 7, 2026, 3:57 p.m.
Created at: April 9, 2026, 10:19 p.m.