Triple

T344442
Position Surface form Disambiguated ID Type / Status
Subject easyJet E6907 entity
Predicate hub P423 FINISHED
Object Berlin Brandenburg Airport E34245 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: Berlin Brandenburg Airport | Statement: [easyJet, hub, Berlin Brandenburg Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berlin Brandenburg Airport
Context triple: [easyJet, hub, Berlin Brandenburg Airport]
  • A. Berlin Brandenburg Airport chosen
    Berlin Brandenburg Airport is the main international airport serving Germany’s capital region, designed to replace and consolidate Berlin’s former commercial airports.
  • B. Tegel
    Tegel is a locality in the Reinickendorf borough of Berlin, Germany, historically known for its manor associated with the Humboldt family and later for the former Berlin Tegel Airport.
  • C. Tempelhof Airport
    Tempelhof Airport is a historic Berlin airfield best known as a central hub of the Berlin Airlift during the Cold War.
  • D. Gatow Airfield
    Gatow Airfield is a former military airbase in Berlin best known for its crucial role as a hub for Allied transport aircraft during the Berlin Airlift.
  • E. Berlin
    Berlin is the capital and largest city of Germany, historically significant as a focal point of Cold War tensions and a major cultural, political, and economic center in Europe.
  • 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_69a2e7951ba08190960e90823b5078f3 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2eb01261c81909280128b5ce75eff completed Feb. 28, 2026, 1:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3d4ea324c8190acd07727ca0ac193 completed March 1, 2026, 5:55 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.