Triple

T2229912
Position Surface form Disambiguated ID Type / Status
Subject Lufthansa E48740 entity
Predicate primaryHub P394 FINISHED
Object Frankfurt Airport E95589 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: Frankfurt Airport | Statement: [Lufthansa, primaryHub, Frankfurt Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frankfurt Airport
Context triple: [Lufthansa, primaryHub, Frankfurt Airport]
  • A. Frankfurt Airport chosen
    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.
  • B. 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.
  • C. 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.
  • D. 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.
  • E. Hannover Airport
    Hannover Airport is an international airport serving the city of Hanover in northern Germany, handling passenger and cargo flights 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_69a88aa51b388190949868ec9766e587 completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc069e0ac8190bcda8cba9f5c7a5d completed March 7, 2026, 6:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae656a1f788190a08c3283bfedb6f4 completed March 9, 2026, 6:15 a.m.
Created at: March 4, 2026, 7:47 p.m.