Triple

T3748690
Position Surface form Disambiguated ID Type / Status
Subject Bern E81271 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: [Bern, hasAirport, Bern Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bern Airport
Context triple: [Bern, 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. Aalborg Airport
    Aalborg Airport is an international airport in northern Denmark serving the city of Aalborg and the surrounding region with domestic and European flights.
  • D. Reykjavík Airport
    Reykjavík Airport is a domestic and regional airport located near the center of Iceland’s capital, serving as a key hub for internal flights and short-haul connections to nearby destinations.
  • E. Helsinki Airport
    Helsinki Airport is Finland’s main international air hub, located near Helsinki and serving as a major gateway between Europe and Asia.
  • 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_69ad8b19b7b08190a6188804e99c53e9 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcb6bf95c81909796fbc84995ae05 completed March 8, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4db2f5e9881908c10feafbb569f48 completed March 14, 2026, 3:51 a.m.
Created at: March 8, 2026, 3:35 p.m.