Triple

T539638
Position Surface form Disambiguated ID Type / Status
Subject Black Sea E12600 entity
Predicate receivesRiver P4359 FINISHED
Object Dnieper E41855 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: Dnieper | Statement: [Black Sea, receivesRiver, Dnieper]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dnieper
Context triple: [Black Sea, receivesRiver, Dnieper]
  • A. Dnieper chosen
    The Dnieper is a major Eastern European river that flows through Russia, Belarus, and Ukraine before emptying into the Black Sea.
  • B. Dniester
    The Dniester is a major river in Eastern Europe that flows through Ukraine and Moldova before emptying into the Black Sea.
  • C. Kuban River
    The Kuban River is a major river in the North Caucasus region of Russia that flows through the Krasnodar Krai before emptying into the Sea of Azov.
  • D. Daugava River
    The Daugava River is a major Eastern European river flowing through Russia, Belarus, and Latvia before emptying into the Baltic Sea.
  • E. Vistula River
    The Vistula River is Poland’s longest and most important river, flowing from the Carpathian Mountains to the Baltic Sea and passing through major cities such as Kraków and Warsaw.
  • 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_69a49334226c81908b0ea1689ef6aa3f completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a496dd31c88190b3114805aa31931c completed March 1, 2026, 7:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4efca0f888190add0fe7ab325bfdf completed March 2, 2026, 2:02 a.m.
Created at: March 1, 2026, 7:32 p.m.