Triple

T19034813
Position Surface form Disambiguated ID Type / Status
Subject Kirovo E465837 entity
Predicate hasCurrentName P1213 FINISHED
Object Kropyvnytskyi NE NERFINISHED

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: Kropyvnytskyi | Statement: [Kirovo, hasCurrentName, Kropyvnytskyi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kropyvnytskyi
Context triple: [Kirovo, hasCurrentName, Kropyvnytskyi]
  • A. Kropyvnytskyi chosen
    Kropyvnytskyi is a regional city in central Ukraine known as an important administrative, cultural, and transportation center.
  • B. Vinnytsia
    Vinnytsia is a major city in central Ukraine known as an important administrative, economic, and cultural center on the Southern Bug River.
  • C. Khmelnytskyi
    Khmelnytskyi is a regional city in western Ukraine known as an important administrative, economic, and cultural center.
  • D. Ivano-Frankivsk
    Ivano-Frankivsk is a historic city in western Ukraine known as a cultural, economic, and administrative center of the Carpathian region.
  • E. Чернігів
    Чернігів — одне з найстаріших міст України, розташоване на півночі країни над Десною, відоме своєю давньоруською історією та архітектурними пам’ятками.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dd0359648190bc2a9202c5cf29d2 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5d74295b88190b1c4621735a06223 completed April 20, 2026, 7:35 a.m.
Created at: April 10, 2026, 12:02 p.m.