Triple

T19629816
Position Surface form Disambiguated ID Type / Status
Subject Central Economic Region E471235 entity
Predicate hasMajorCity P316 FINISHED
Object Smolensk 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: Smolensk | Statement: [Central Economic Region, hasMajorCity, Smolensk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Smolensk
Context triple: [Central Economic Region, hasMajorCity, Smolensk]
  • A. Smolensk chosen
    Smolensk is a historic city in western Russia near the Belarusian border, known for its strategic location and centuries-old fortifications.
  • B. Podolsk
    Podolsk is a major industrial city and former center of machine-building located just south of Moscow in western Russia.
  • C. Smolenskaya
    Smolenskaya is a Moscow Metro station on the Arbatsko–Pokrovskaya and Filyovskaya lines, located near the historic Arbat district.
  • D. Borisoglebsk
    Borisoglebsk is a small Russian city known for its historical architecture and location on the Vorona River in southwestern Russia.
  • E. Trubchevsk
    Trubchevsk is a historic town in western Russia, known as a former medieval center and namesake of the Principality of Trubchevsk.
  • 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_69d8e511f28481909f4bc3ea9191e54a completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e64101a0448190ba19f8917ae85dd6 completed April 20, 2026, 3:06 p.m.
Created at: April 10, 2026, 1:44 p.m.