Triple

T17003534
Position Surface form Disambiguated ID Type / Status
Subject Kursky railway terminal E412508 entity
Predicate connectsToCity P4245 FINISHED
Object Belgorod 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: Belgorod | Statement: [Kursky railway terminal, connectsToCity, Belgorod]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Belgorod
Context triple: [Kursky railway terminal, connectsToCity, Belgorod]
  • A. Belgorod chosen
    Belgorod is a city in western Russia near the Ukrainian border, historically significant as a strategic site of major World War II battles and offensives.
  • B. Voronezh
    Voronezh is a major city in southwestern Russia, situated on the Voronezh River and serving as an important cultural, industrial, and transportation center.
  • C. Oryol
    Oryol is a historic city in western Russia situated on the Oka River, known as a regional cultural and administrative center.
  • D. Oryol
    Oryol was a notable warship of the Imperial Russian Navy, recognized for its role in Russia’s early modern naval history.
  • E. Tambov
    Tambov is a city in western Russia known as an administrative, cultural, and industrial center of the Tambov Oblast.
  • 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_69d886cb581c8190ab05f4b429c9cd85 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d380f968819097b59e7bac333ea4 completed April 18, 2026, 6:54 p.m.
Created at: April 10, 2026, 5:32 a.m.