Triple

T10036735
Position Surface form Disambiguated ID Type / Status
Subject Larisa Shepitko E205188 entity
Predicate spouse P13 FINISHED
Object Elem Klimov E252430 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: Elem Klimov | Statement: [Larisa Shepitko, spouse, Elem Klimov]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elem Klimov
Context triple: [Larisa Shepitko, spouse, Elem Klimov]
  • A. Elem Klimov chosen
    Elem Klimov was a Soviet film director best known internationally for his harrowing World War II drama "Come and See."
  • B. Oleg Klimov
    Oleg Klimov is a researcher known for his contributions to the development and analysis of Proximal Policy Optimization (PPO) algorithms in reinforcement learning.
  • C. Yuri Boldyrev
    Yuri Boldyrev is a Russian politician and public figure best known as one of the founders of the liberal opposition party Yabloko.
  • D. Eldar Ryazanov
    Eldar Ryazanov was a renowned Soviet and Russian film director and screenwriter, celebrated for his sharp-witted comedies and satirical portrayals of everyday life.
  • E. Andrei Zelentsov
    Andrei Zelentsov was a Soviet military commander best known for leading Red Army forces during the Winter War against Finland, including in the Battle of Suomussalmi.
  • 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_69ca834f70e88190b2d74828b7767ec1 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdce4bb3408190ac5dae4718ef7cad completed April 2, 2026, 2:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69dbac7a42348190a86fa5a97e3d36ca completed April 12, 2026, 2:30 p.m.
Created at: March 30, 2026, 8:55 p.m.