Triple

T5353182
Position Surface form Disambiguated ID Type / Status
Subject Gothenburg City Airport E102625 entity
Predicate formerName P65 FINISHED
Object Säve Airport E435620 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: Säve Airport | Statement: [Gothenburg City Airport, formerName, Säve Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Säve Airport
Context triple: [Gothenburg City Airport, formerName, Säve Airport]
  • A. Ronneby Airport
    Ronneby Airport is a regional airport in southern Sweden serving the Ronneby and Blekinge area with domestic flights and operated as part of the national airport network.
  • B. Landvetter Airport
    Landvetter Airport is an international airport serving the Gothenburg region in western Sweden and is one of the country’s major aviation hubs.
  • C. Torslanda Airport chosen
    Torslanda Airport was the former main airport serving Gothenburg, Sweden, before being superseded by Gothenburg Landvetter Airport.
  • D. Luleå Airport
    Luleå Airport is a major civilian and military airport in northern Sweden serving the city of Luleå and the wider Norrbotten region.
  • E. Karlskoga airfield
    Karlskoga airfield is a small regional airport serving the town of Karlskoga in Sweden, primarily used for general aviation and local air traffic.
  • 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_69bd43d8f7248190b64c140734b5c9a8 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd861327288190b3f2720ce81e0de6 completed March 20, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf9adecb988190a5f289259cdd2f98 completed March 22, 2026, 7:31 a.m.
Created at: March 20, 2026, 2:01 p.m.