Triple

T20563282
Position Surface form Disambiguated ID Type / Status
Subject S1 (Munich S-Bahn) E504897 entity
Predicate serves P98 FINISHED
Object Munich 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 Airport | Statement: [S1 (Munich S-Bahn), serves, Munich Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Munich Airport
Context triple: [S1 (Munich S-Bahn), serves, Munich Airport]
  • A. 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.
  • B. 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.
  • C. Frankfurt Airport
    Frankfurt Airport is one of Europe’s busiest international aviation hubs, serving as a major global gateway and primary airport for the city of Frankfurt am Main in Germany.
  • D. Hamburg Airport
    Hamburg Airport is an international airport in northern Germany serving the city of Hamburg and the surrounding region as a major passenger and cargo hub.
  • E. Munich Airport Center
    Munich Airport Center is a multifunctional commercial and service complex at Munich Airport featuring shops, restaurants, event spaces, and connections between the airport’s terminals and transport links.
  • 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_69e0b4b6587c8190aee63dc7cff244ea completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6a7a0a0488190a534050b40ff47da completed April 20, 2026, 10:24 p.m.
Created at: April 16, 2026, 11:39 a.m.