Triple

T10982897
Position Surface form Disambiguated ID Type / Status
Subject Central Federal Okrug E259552 entity
Predicate includesCity P3207 FINISHED
Object Tambov E514739 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: Tambov | Statement: [Central Federal Okrug, includesCity, Tambov]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tambov
Context triple: [Central Federal Okrug, includesCity, Tambov]
  • A. Tambov chosen
    Tambov is a city in western Russia known as an administrative, cultural, and industrial center of the Tambov Oblast.
  • B. Oryol
    Oryol was a notable warship of the Imperial Russian Navy, recognized for its role in Russia’s early modern naval history.
  • C. Belgorod
    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.
  • D. Lipetsk
    Lipetsk is a major industrial city in western Russia, known for its steel production and status as the administrative center of Lipetsk Oblast.
  • E. Voronezh
    Voronezh is a major city in southwestern Russia, situated on the Voronezh River and serving as an important cultural, industrial, and transportation 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_69d6aa895f4c8190887a15460ef622f4 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d772eb518c8190a885a417815f2ff6 completed April 9, 2026, 9:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef8185c6e08190949020a80c24f2b8 completed April 27, 2026, 3:32 p.m.
Created at: April 8, 2026, 9:24 p.m.