Triple

T15557596
Position Surface form Disambiguated ID Type / Status
Subject Tikhvin Defensive Operation E370910 entity
Predicate location P40 FINISHED
Object Tikhvin E244096 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: Tikhvin | Statement: [Tikhvin Defensive Operation, location, Tikhvin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tikhvin
Context triple: [Tikhvin Defensive Operation, location, Tikhvin]
  • A. Tikhvin chosen
    Tikhvin is a historic town in northwestern Russia known for its ancient monastery, religious icons, and role as a regional cultural and industrial center.
  • B. Volzhsk
    Volzhsk is an industrial city in the Republic of Mari El, Russia, located on the Volga River and known for its manufacturing and river transport significance.
  • C. Volzhsky
    Volzhsky is a major industrial city in southwestern Russia located across the Volga River from Volgograd.
  • D. Kamyshin
    Kamyshin is a significant industrial and river port city on the Volga River in southwestern Russia.
  • E. Zaraysk
    Zaraysk is a historic town in Moscow Oblast, Russia, known for its well-preserved medieval kremlin and role as a former regional administrative center.
  • 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_69d85cc6cf40819091f4a5facee1ebe6 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04dda3ab88190ab383333ce69fe8f completed April 16, 2026, 2:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01953c493c819084850ab8e7f0d261 completed May 11, 2026, 8:37 a.m.
Created at: April 10, 2026, 4:09 a.m.