Triple

T622879
Position Surface form Disambiguated ID Type / Status
Subject Stockholm E14550 entity
Predicate hasAirport P105 FINISHED
Object Stockholm Bromma Airport E19491 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: Stockholm Bromma Airport | Statement: [Stockholm, hasAirport, Stockholm Bromma Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Stockholm Bromma Airport
Context triple: [Stockholm, hasAirport, Stockholm Bromma Airport]
  • A. Stockholm Bromma Airport chosen
    Stockholm Bromma Airport is a regional airport near central Stockholm, Sweden, primarily serving domestic and short-haul European flights.
  • B. Stockholm Arlanda Airport
    Stockholm Arlanda Airport is Sweden’s largest and busiest international airport, serving as the primary gateway to Stockholm and a major hub for Scandinavian and European air traffic.
  • C. 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.
  • D. Oslo Airport, Gardermoen
    Oslo Airport, Gardermoen is Norway’s main international airport and the primary aviation hub serving the Oslo region.
  • E. Faro Airport
    Faro Airport is the main international airport serving Portugal’s Algarve region, handling millions of tourists each year who visit its popular coastal resorts.
  • 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_69a4934b17c881909ace8270e8ddd202 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49e41753881909f0faed720cc31bc completed March 1, 2026, 8:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a567012e9c81909d502e29fff35750 completed March 2, 2026, 10:31 a.m.
Created at: March 1, 2026, 7:35 p.m.