Triple

T28382100
Position Surface form Disambiguated ID Type / Status
Subject Blowing Up Russia: Terror from Within E718914 entity
Predicate author P4 FINISHED
Object Yuri Felshtinsky
Yuri Felshtinsky is a Russian-American historian and writer known for his controversial works on Soviet and post-Soviet security services and alleged state-sponsored terrorism.
E2295151 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: Yuri Felshtinsky | Statement: [Blowing Up Russia: Terror from Within, author, Yuri Felshtinsky]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Yuri Felshtinsky
Triple: [Blowing Up Russia: Terror from Within, author, Yuri Felshtinsky]
Generated description
Yuri Felshtinsky is a Russian-American historian and writer known for his controversial works on Soviet and post-Soviet security services and alleged state-sponsored terrorism.

Provenance (5 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_69eff6ef211081909d31d9be5f5567e6 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64cb6c8708190b0a947d7da770920 completed May 2, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a7d104d03588190adaaa1f0a3687d1f completed Aug. 13, 2026, 12:31 a.m.
NEDg Description generation batch_6a7d10b3b014819099710f82c31887cb completed Aug. 13, 2026, 12:32 a.m.
NED2 Entity disambiguation (via description) batch_6a7d1184367c8190bb6b688de8b5ec8b completed Aug. 13, 2026, 12:36 a.m.
Created at: April 28, 2026, 1:07 a.m.