Triple

T22480693
Position Surface form Disambiguated ID Type / Status
Subject Rowno E555755 entity
Predicate historicalNameOf P65 FINISHED
Object Rivne 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: Rivne | Statement: [Rowno, historicalNameOf, Rivne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rivne
Context triple: [Rowno, historicalNameOf, Rivne]
  • A. Rivne chosen
    Rivne is a city in western Ukraine that serves as an important regional administrative, economic, and cultural center.
  • B. Zbarazh
    Zbarazh is a historic town in western Ukraine known for its medieval castle and role in regional political and military history.
  • C. Vinnytsia
    Vinnytsia is a major city in central Ukraine known as an important administrative, economic, and cultural center on the Southern Bug River.
  • D. Ternopil
    Ternopil is a city in western Ukraine known as a regional cultural and economic center with a historic old town and a picturesque lakeside setting.
  • E. Drohobych
    Drohobych is a historic city in western Ukraine known for its medieval architecture, salt production heritage, and association with writer and artist Bruno Schulz.
  • 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_69e11e53897c819088863779f8c50bb0 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15c3836a08190b6f0d88b94cb80a3 completed April 29, 2026, 1:17 a.m.
Created at: April 16, 2026, 8:49 p.m.