Triple

T15968787
Position Surface form Disambiguated ID Type / Status
Subject Spenge E387265 entity
Predicate hasTwinTown P919 FINISHED
Object Pechory E1025402 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: Pechory | Statement: [Spenge, hasTwinTown, Pechory]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pechory
Context triple: [Spenge, hasTwinTown, Pechory]
  • A. Pechory chosen
    Pechory is a historic town in western Russia near the Estonian border, known for its religious significance and well-preserved medieval architecture.
  • B. Dzyatlava
    Dzyatlava is a small historic town in present-day Belarus, known for its Jewish heritage and as the birthplace of the influential rabbi and ethicist Yisrael Meir Kagan (the Chofetz Chaim).
  • C. Borisoglebsk
    Borisoglebsk is a small Russian city known for its historical architecture and location on the Vorona River in southwestern Russia.
  • D. Volochysk
    Volochysk is a town in western Ukraine known as a local administrative and transportation center near the border with Ternopil Oblast.
  • E. Dzyarzhynsk
    Dzyarzhynsk is a town in Belarus known for its proximity to Dzyarzhynskaya Hara, the country’s highest point.
  • 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_69d86da94ccc819083d187f5dc6a123e completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1572847f08190830e30125e829766 completed April 16, 2026, 9:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffc3c5a7d48190891e69314e67e9af completed May 9, 2026, 11:31 p.m.
Created at: April 10, 2026, 4:54 a.m.