Triple

T20272832
Position Surface form Disambiguated ID Type / Status
Subject EDDM E502932 entity
Predicate replacedAirport P29669 FINISHED
Object Munich-Riem 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: Munich-Riem Airport | Statement: [EDDM, replacedAirport, Munich-Riem Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Munich-Riem Airport
Context triple: [EDDM, replacedAirport, Munich-Riem Airport]
  • A. Nuremberg Airport
    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. Munich Airport chosen
    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.
  • C. 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.
  • D. 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.
  • 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_69e0b4b0e79c8190bd61f22ef1329fa8 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e675dff3c4819098ba45eba4e7b296 completed April 20, 2026, 6:52 p.m.
Created at: April 16, 2026, 10:15 a.m.