Triple

T21337419
Position Surface form Disambiguated ID Type / Status
Subject Tychy E526085 entity
Predicate hasTwinTown P919 FINISHED
Object Ternopil 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: Ternopil | Statement: [Tychy, hasTwinTown, Ternopil]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ternopil
Context triple: [Tychy, hasTwinTown, Ternopil]
  • A. Ternopil chosen
    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.
  • B. Ivano-Frankivsk
    Ivano-Frankivsk is a historic city in western Ukraine known as a cultural, economic, and administrative center of the Carpathian region.
  • C. Khmelnytskyi
    Khmelnytskyi is a regional city in western Ukraine known as an important administrative, economic, and cultural center.
  • D. Vinnytsia
    Vinnytsia is a major city in central Ukraine known as an important administrative, economic, and cultural center on the Southern Bug River.
  • 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_69e0b51c33048190ab27cede74ef798c completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e898da015081909e83fb62cf166b9a completed April 22, 2026, 9:46 a.m.
Created at: April 16, 2026, 4:43 p.m.