Triple

T5698356
Position Surface form Disambiguated ID Type / Status
Subject Lufthansa CityLine E125595 entity
Predicate mainHub P423 FINISHED
Object Munich Airport E117566 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: Munich Airport | Statement: [Lufthansa CityLine, mainHub, Munich Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Munich Airport
Context triple: [Lufthansa CityLine, mainHub, 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. 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.
  • 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_69c0082c96988190b3a6a201edce472a completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c0240ecef48190bdef10b38ecb2bd0 completed March 22, 2026, 5:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a5996a08190860cb5fab57c31b5 completed March 22, 2026, 9:08 p.m.
Created at: March 22, 2026, 3:45 p.m.