Triple

T1991309
Position Surface form Disambiguated ID Type / Status
Subject Rossiya Airlines E43256 entity
Predicate focusCity P164 FINISHED
Object Simferopol E10809 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: Simferopol | Statement: [Rossiya Airlines, focusCity, Simferopol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Simferopol
Context triple: [Rossiya Airlines, focusCity, Simferopol]
  • A. Simferopol chosen
    Simferopol is the administrative and cultural center of Crimea, known as a key regional hub for transportation, education, and industry.
  • B. Yevpatoria
    Yevpatoria is a historic resort and port city on the western coast of Crimea, known for its beaches, therapeutic mud treatments, and diverse cultural heritage.
  • C. Mykolaiv
    Mykolaiv is a major shipbuilding and industrial city in southern Ukraine located near the Black Sea.
  • D. Donetsk
    Donetsk is a major industrial city in eastern Ukraine, historically known for its coal mining and steel production.
  • E. Simferopol–Alushta
    Simferopol–Alushta is the central mountain-crossing section of the Crimean trolleybus route that links the regional capital Simferopol with the Black Sea resort town of Alushta.
  • 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_69a88714cf2c819081644be450b8356e completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb8451fe8819093531052f4533c36 completed March 7, 2026, 5:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae0ad7c254819091159c5362e7a293 completed March 8, 2026, 11:48 p.m.
Created at: March 4, 2026, 7:37 p.m.