Triple

T1552871
Position Surface form Disambiguated ID Type / Status
Subject Crimean trolleybus line E33132 entity
Predicate terminus P388 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: [Crimean trolleybus line, terminus, Simferopol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Simferopol
Context triple: [Crimean trolleybus line, terminus, 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. Odesa
    Odesa is a major port city on the Black Sea in southern Ukraine, known for its historic architecture, multicultural heritage, and key economic and cultural role in the country.
  • 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_69a885ee6db8819099502bc5ce8af881 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a9086c96bc8190bed8b290fa16f21b completed March 5, 2026, 4:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad51ab8e208190b4bcf03e9a661070 completed March 8, 2026, 10:38 a.m.
Created at: March 4, 2026, 7:26 p.m.