Triple

T11840990
Position Surface form Disambiguated ID Type / Status
Subject Western Front (Red Army) E281650 entity
Predicate headquartersLocation P62 FINISHED
Object Vyazma E366953 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: Vyazma | Statement: [Western Front (Red Army), headquartersLocation, Vyazma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vyazma
Context triple: [Western Front (Red Army), headquartersLocation, Vyazma]
  • A. Vyazma chosen
    Vyazma is a historic town in Smolensk Oblast, western Russia, known for its strategic military significance, particularly during World War II.
  • B. Rzhev
    Rzhev is a historic town in western Russia known for its strategic location on the Volga River and as the site of major World War II battles.
  • C. Kozelsk
    Kozelsk is a historic town in western Russia known for its medieval defenses and location within Kaluga Oblast.
  • D. Borisoglebsk
    Borisoglebsk is a small Russian city known for its historical architecture and location on the Vorona River in southwestern Russia.
  • E. Borovsk
    Borovsk is a historic town in western Russia known for its well-preserved architecture, monasteries, and role in regional trade and culture.
  • 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_69d6ab276f8c8190b1966a0ef11349ac completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a658f918819092c2db05fe2ab0ce completed April 10, 2026, 7:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69f2814210e48190821fca390dc7e312 completed April 29, 2026, 10:08 p.m.
Created at: April 8, 2026, 9:43 p.m.