Triple

T21318104
Position Surface form Disambiguated ID Type / Status
Subject Runway 10/28 E525531 entity
Predicate airportServed P4363 FINISHED
Object Nuremberg 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: Nuremberg Airport | Statement: [Runway 10/28, airportServed, Nuremberg Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nuremberg Airport
Context triple: [Runway 10/28, airportServed, Nuremberg Airport]
  • A. Nuremberg Airport chosen
    Nuremberg Airport is an international airport in northern Bavaria, Germany, serving the city of Nuremberg and the surrounding Franconia region with passenger and cargo flights.
  • B. Augsburg Airport
    Augsburg Airport is a regional airport in Bavaria, Germany, serving the city of Augsburg and its surrounding area with general aviation and limited commercial services.
  • C. Stuttgart Airport
    Stuttgart Airport is the international airport serving the city of Stuttgart in southwestern Germany, handling both passenger and cargo traffic for the region.
  • D. Munich Airport
    Munich Airport is a major international aviation hub in Bavaria, Germany, serving as one of the country’s busiest airports and a key base for Lufthansa.
  • E. Dresden Airport
    Dresden Airport is an international airport serving the city of Dresden in eastern Germany, offering passenger and cargo flights and connecting the region to major European destinations.
  • 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_69e0b51ad810819098c12392c8e55f6c completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e77ece1c348190aaa9c52474b57b2f completed April 21, 2026, 1:42 p.m.
Created at: April 16, 2026, 4:36 p.m.