Triple

T15998816
Position Surface form Disambiguated ID Type / Status
Subject Oksana Baiul E388043 entity
Predicate trainedIn P2424 FINISHED
Object Odesa E38290 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: Odesa | Statement: [Oksana Baiul, trainedIn, Odesa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Odesa
Context triple: [Oksana Baiul, trainedIn, Odesa]
  • A. Odesa chosen
    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.
  • B. Odessa
    Odessa is a mid-sized city in western Texas known for its oil industry, high school football culture, and role in the Permian Basin energy region.
  • C. Odessa
    Odessa is a central, devoutly religious housekeeper in James Baldwin’s play "The Amen Corner," known for her loyalty and moral grounding amid the story’s family and church conflicts.
  • D. Mykolaiv
    Mykolaiv is a major shipbuilding and industrial city in southern Ukraine located near the Black Sea.
  • E. 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.
  • 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_69d86daa562c81908aacc179c0fe8fb5 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e157893ebc8190acb75ee05e450fae completed April 16, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00077a54c88190ab94a558bfaecf2f completed May 10, 2026, 4:20 a.m.
Created at: April 10, 2026, 4:55 a.m.