Triple

T19995802
Position Surface form Disambiguated ID Type / Status
Subject Margraten E494188 entity
Predicate nearbyAirport P350 FINISHED
Object Maastricht Aachen Airport NE NERFINISHED

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: Maastricht Aachen Airport | Statement: [Margraten, nearbyAirport, Maastricht Aachen Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maastricht Aachen Airport
Context triple: [Margraten, nearbyAirport, Maastricht Aachen Airport]
  • A. Maastricht Aachen Airport chosen
    Maastricht Aachen Airport is a regional international airport in the southeastern Netherlands serving the cities of Maastricht and Aachen and the surrounding Limburg region.
  • B. Liège Airport
    Liège Airport is a major international cargo and passenger airport in eastern Belgium, known as one of Europe’s leading freight hubs.
  • C. Eindhoven Airport
    Eindhoven Airport is a major regional airport in the Netherlands that serves as a key hub for low-cost and European short-haul flights.
  • D. Antwerp International Airport
    Antwerp International Airport is a small regional airport in Antwerp, Belgium, primarily serving short-haul European flights and general aviation.
  • E. Brussels Airport
    Brussels Airport is the main international airport serving Brussels and one of Belgium’s busiest air transport hubs for passengers and cargo.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da626b2d748190886981ea90c8b2ea completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e65fe3fb288190a935c334e8a5d54e completed April 20, 2026, 5:18 p.m.
Created at: April 11, 2026, 3:32 p.m.