Triple

T3232913
Position Surface form Disambiguated ID Type / Status
Subject Alushta E67783 entity
Predicate locatedSouthOf P9676 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: [Alushta, locatedSouthOf, Simferopol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Simferopol
Context triple: [Alushta, locatedSouthOf, 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. Kherson
    Kherson is a port city in southern Ukraine near the Black Sea, historically significant as a shipbuilding and industrial center and strategically important due to its location on the Dnieper River.
  • D. Mykolaiv
    Mykolaiv is a major shipbuilding and industrial city in southern Ukraine located near the Black Sea.
  • E. Donetsk
    Donetsk is a major industrial city in eastern Ukraine, historically known for its coal mining and steel production.
  • 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_69ad858c61888190a31196310d9b30b5 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaedb718c8190aae12f763033713a completed March 8, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4cdc422d08190aa67bc39619c3290 completed March 14, 2026, 2:53 a.m.
Created at: March 8, 2026, 3:08 p.m.