Triple

T1047247
Position Surface form Disambiguated ID Type / Status
Subject Oslo Airport, Gardermoen E22608 entity
Predicate alsoKnownAs P39 FINISHED
Object Oslo Airport E22608 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: Oslo Airport | Statement: [Oslo Airport, Gardermoen, alsoKnownAs, Oslo Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oslo Airport
Context triple: [Oslo Airport, Gardermoen, alsoKnownAs, Oslo Airport]
  • A. Oslo Airport, Gardermoen chosen
    Oslo Airport, Gardermoen is Norway’s main international airport and the primary aviation hub serving the Oslo region.
  • 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. Oslo Airport Station
    Oslo Airport Station is the main railway station serving Oslo Airport, Gardermoen, providing high-speed and regional train connections between the airport and the rest of Norway.
  • D. Gothenburg Landvetter Airport
    Gothenburg Landvetter Airport is the main international airport serving the Gothenburg region in western Sweden.
  • E. Gothenburg City Airport
    Gothenburg City Airport is a regional airport serving the Gothenburg area in western Sweden.
  • 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_69a493da02e081908c13ff5e02a0fe7a completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b84d30888190b66f7245d781957d completed March 1, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac53864a20819081fc59e7102a6e00 completed March 7, 2026, 4:34 p.m.
Created at: March 1, 2026, 7:42 p.m.