Triple

T16460324
Position Surface form Disambiguated ID Type / Status
Subject Belp E399788 entity
Predicate hasAirport P105 FINISHED
Object Bern Airport E99408 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: Bern Airport | Statement: [Belp, hasAirport, Bern Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bern Airport
Context triple: [Belp, hasAirport, Bern Airport]
  • A. Bern Airport chosen
    Bern Airport is a small regional airport in Switzerland serving the city of Bern and offering domestic and limited international flights.
  • B. Copenhagen Airport Kastrup
    Copenhagen Airport Kastrup is Denmark’s largest and busiest international airport, serving as the main air hub for Copenhagen and much of Scandinavia.
  • C. Hans Christian Andersen Airport
    Hans Christian Andersen Airport is a regional airport serving the city of Odense on the island of Funen in Denmark.
  • 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. Bost Airport
    Bost Airport is a regional airport serving the city of Lashkargah in Helmand Province, Afghanistan.
  • 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_69d87f2dac988190b74d6e185fa88ba4 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e32d80e66c8190b2b3199efe9cfaa1 completed April 18, 2026, 7:06 a.m.
NED1 Entity disambiguation (via context triple) batch_6a005817fa088190a0eb85016fe5afc4 completed May 10, 2026, 10:04 a.m.
Created at: April 10, 2026, 5:10 a.m.