Triple

T22696952
Position Surface form Disambiguated ID Type / Status
Subject PKC E561204 entity
Predicate alsoKnownAs P39 FINISHED
Object Yelizovo Airport NE NERFINISHED

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: Yelizovo Airport | Statement: [PKC, alsoKnownAs, Yelizovo Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yelizovo Airport
Context triple: [PKC, alsoKnownAs, Yelizovo Airport]
  • A. Yelizovo Airport chosen
    Yelizovo Airport is the main civil and military airport serving the city of Petropavlovsk-Kamchatsky and the Kamchatka Peninsula in Russia.
  • B. Yemelyanovo International Airport
    Yemelyanovo International Airport is a major airport in Siberia serving the city of Krasnoyarsk and acting as an important regional hub for both domestic and international flights in Russia.
  • C. Baratayevka Airport
    Baratayevka Airport is a regional airport serving the city of Ulyanovsk in Russia.
  • D. Spichenkovo Airport
    Spichenkovo Airport is a regional airport serving the city of Novokuznetsk in Russia’s Kemerovo Oblast, providing domestic flights and access to the Kuzbass industrial region.
  • E. Ramenskoye Airport
    Ramenskoye Airport is a Russian airfield near Zhukovsky, Moscow Oblast, known for serving as a major flight testing center and hosting the MAKS International Aviation and Space Salon.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e2454e615481909c177440be559d2c completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f1789f26848190bcc5a99e3ed909e7 completed April 29, 2026, 3:18 a.m.
Created at: April 17, 2026, 3:14 p.m.