Triple

T10413347
Position Surface form Disambiguated ID Type / Status
Subject SAS E245449 entity
Predicate hubAirport P8171 FINISHED
Object Copenhagen Airport E67497 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: Copenhagen Airport | Statement: [SAS, hubAirport, Copenhagen Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Copenhagen Airport
Context triple: [SAS, hubAirport, Copenhagen Airport]
  • A. Copenhagen Airport Kastrup chosen
    Copenhagen Airport Kastrup is Denmark’s largest and busiest international airport, serving as the main air hub for Copenhagen and much of Scandinavia.
  • B. Hans Christian Andersen Airport
    Hans Christian Andersen Airport is a regional airport serving the city of Odense on the island of Funen in Denmark.
  • C. Bern Airport
    Bern Airport is a small regional airport in Switzerland serving the city of Bern and offering domestic and limited international flights.
  • D. Midtjyllands Airport
    Midtjyllands Airport is a regional airport in central Jutland, Denmark, serving domestic and limited international flights for the surrounding Midtjylland area.
  • E. Aarhus Airport
    Aarhus Airport is a regional international airport serving the city of Aarhus and the surrounding area in eastern Jutland, Denmark.
  • 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_69d381be340c8190b05998703d42d224 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4ea0ec6fc8190a71af759226a3cba completed April 7, 2026, 11:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69d87e9b86648190b83eb5261c9a7b97 completed April 10, 2026, 4:37 a.m.
Created at: April 6, 2026, 12:10 p.m.