Triple

T16680582
Position Surface form Disambiguated ID Type / Status
Subject Lithuanian Air Force E405326 entity
Predicate operates P24 FINISHED
Object Kaunas Air Base E89349 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: Kaunas Air Base | Statement: [Lithuanian Air Force, operates, Kaunas Air Base]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kaunas Air Base
Context triple: [Lithuanian Air Force, operates, Kaunas Air Base]
  • A. Šiauliai Air Base
    Šiauliai Air Base is a major Lithuanian military airfield that serves as a key NATO hub for regional air defense and operations.
  • B. Kaunas Airport chosen
    Kaunas Airport is an international airport in Lithuania serving the city of Kaunas and functioning as one of the country’s main commercial and low-cost airline hubs.
  • C. Vilnius Airport
    Vilnius Airport is the main international airport serving Lithuania’s capital, handling the majority of the region’s passenger and air traffic.
  • D. Malbork Air Base
    Malbork Air Base is a Polish military airfield that serves as a key NATO outpost for air defense missions over the Baltic region.
  • E. Ventspils International Airport
    Ventspils International Airport is a regional airport in Ventspils, Latvia, serving as an air transport hub for the city and surrounding Kurzeme 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_69d8838c28748190b3f5967c743940ab completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37d6f5cf481909e7628bbaa884e5a completed April 18, 2026, 12:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a009d32e7b48190b7dd4660bed4789d completed May 10, 2026, 2:58 p.m.
Created at: April 10, 2026, 5:19 a.m.