Triple

T7993132
Position Surface form Disambiguated ID Type / Status
Subject Simferopol–Alushta E186056 entity
Predicate endPoint P390 FINISHED
Object Alushta E67783 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: Alushta | Statement: [Simferopol–Alushta, endPoint, Alushta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alushta
Context triple: [Simferopol–Alushta, endPoint, Alushta]
  • A. Alushta chosen
    Alushta is a resort town on the southern coast of Crimea, known for its beaches, mild climate, and role as a popular Black Sea tourist destination.
  • B. Liman
    Liman is a surname most notably associated with American film director and producer Doug Liman.
  • C. Vineta
    Vineta is a legendary medieval Baltic Sea trading city, often associated with the island of Wolin and famed in myth as a wealthy metropolis lost beneath the waves.
  • D. Kerch
    Kerch is a historic port city in eastern Crimea, strategically located on the Kerch Strait linking the Black Sea and the Sea of Azov.
  • E. Gökçeada
    Gökçeada is the largest island of Turkey, located in the northern Aegean Sea and known for its Greek heritage, natural landscapes, and traditional villages.
  • 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_69ca829c6c308190ab05b43d234c52b2 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c729afc81909d477b1623ac3f9d completed March 31, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc568ac4e88190b63b4d57c3bd3205 completed March 31, 2026, 11:19 p.m.
Created at: March 30, 2026, 5:16 p.m.