Triple

T5815228
Position Surface form Disambiguated ID Type / Status
Subject Tatyana Tolstaya E128967 entity
Predicate employer P7 FINISHED
Object Russian television E448632 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: Russian television | Statement: [Tatyana Tolstaya, employer, Russian television]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Russian television
Context triple: [Tatyana Tolstaya, employer, Russian television]
  • A. Russian state television chosen
    Russian state television is the network of state-controlled TV channels in Russia that serve as the government’s primary tool for nationwide news, information, and political messaging.
  • B. Soviet television
    Soviet television was the state-controlled broadcasting system of the USSR, responsible for producing and transmitting television programs across the Soviet Union.
  • C. Radio Moscow
    Radio Moscow is an American psychedelic blues-rock band known for its heavy, vintage-inspired sound and guitar-driven performances.
  • D. NTV
    NTV is a major Japanese commercial television network known for its wide range of news, entertainment, and sports programming.
  • E. RU
    RU is the common abbreviation for Radboud University Nijmegen, a major research university located in Nijmegen, the Netherlands.
  • 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_69c0084788848190bcf71f6bc5d71597 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c0336344148190bcf417c0b9617cb9 completed March 22, 2026, 6:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0984c5f14819096dfabce4a83e332 completed March 23, 2026, 1:33 a.m.
Created at: March 22, 2026, 3:53 p.m.