Triple

T4091907
Position Surface form Disambiguated ID Type / Status
Subject Kaunas County E87721 entity
Predicate hasAirport P105 FINISHED
Object Kaunas Airport 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 Airport | Statement: [Kaunas County, hasAirport, Kaunas Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kaunas Airport
Context triple: [Kaunas County, hasAirport, Kaunas Airport]
  • A. 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.
  • B. Riga International Airport
    Riga International Airport is Latvia’s largest and busiest airport, serving as the main air gateway to the capital city of Riga and the surrounding Baltic region.
  • C. Tartu Airport
    Tartu Airport is a regional airport in Estonia serving the city of Tartu and the surrounding area with domestic and limited international flights.
  • D. 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.
  • E. Brest Airport
    Brest Airport is a regional international airport serving the city of Brest in southwestern Belarus, handling passenger and limited cargo flights.
  • 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_69aed94425148190be337845d56fac22 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefcae22a081908af65a960306b78c completed March 9, 2026, 5 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b6cfb288190ac08c3a37327ac9a completed March 14, 2026, 2:06 p.m.
Created at: March 9, 2026, 3:40 p.m.