Triple

T3069153
Position Surface form Disambiguated ID Type / Status
Subject Saratov Oblast E62177 entity
Predicate hasBorderWith P224 FINISHED
Object Tambov Oblast E444723 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 Oblast | Statement: [Saratov Oblast, hasBorderWith, Tambov Oblast]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tambov Oblast
Context triple: [Saratov Oblast, hasBorderWith, Tambov Oblast]
  • A. Tambov Oblast chosen
    Tambov Oblast is a federal subject of central Russia known for its fertile agricultural lands and location along the middle reaches of the Don River.
  • B. Ryazan Oblast
    Ryazan Oblast is a federal subject of central Russia known for its historic cities, agricultural landscapes, and location along the Oka River southeast of Moscow.
  • C. Voronezh Oblast
    Voronezh Oblast is a federal subject of Russia in the country’s southwest, known for its administrative center Voronezh and its role as an important agricultural and industrial region.
  • D. Lipetsk Oblast
    Lipetsk Oblast is a federal subject of western Russia known for its industrial centers, agricultural production, and administrative capital, the city of Lipetsk.
  • E. Penza Oblast
    Penza Oblast is a federal subject of central Russia known for its agricultural economy, mixed forests, and role as a regional industrial and cultural 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_69ad85793e5c8190a358049bc4a98d8c completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada0ffcc208190962cc9edcbf43c31 completed March 8, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8b4d712008190aa25340e1feb0804 completed March 29, 2026, 5:12 a.m.
Created at: March 8, 2026, 3:02 p.m.