Triple

T7741115
Position Surface form Disambiguated ID Type / Status
Subject Krasnodar E175511 entity
Predicate formerName P65 FINISHED
Object Yekaterinodar E387993 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: Yekaterinodar | Statement: [Krasnodar, formerName, Yekaterinodar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yekaterinodar
Context triple: [Krasnodar, formerName, Yekaterinodar]
  • A. Ekaterinodar chosen
    Ekaterinodar, now known as Krasnodar, was a major city in southern Russia that served as an important political and military center in the Kuban region.
  • B. Kirov
    Kirov is the revolutionary pseudonym of Sergei Kirov, a prominent early Soviet political leader and close associate of Joseph Stalin.
  • C. Kirov
    Kirov is a town in Kaluga Oblast, Russia, known as a local administrative and industrial center.
  • D. Arkhangelsk
    Arkhangelsk is a historic port city in northern Russia on the White Sea, long serving as a key maritime gateway and administrative center of the surrounding region.
  • E. Kirovabad
    Kirovabad was the Soviet-era name of the Azerbaijani city now known as Ganja, an important industrial and cultural center in western Azerbaijan.
  • 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_69c6995f9c60819092e386192bd63c6f completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c7035df9348190ad3f3d845207bf4d completed March 27, 2026, 10:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69c93fa2550481908348b8b4dd23d6df completed March 29, 2026, 3:05 p.m.
Created at: March 27, 2026, 4:07 p.m.