Triple

T28219741
Position Surface form Disambiguated ID Type / Status
Subject Botvinnik chess school E711417 entity
Predicate notableStudent P4838 FINISHED
Object Alexander Beliavsky
Alexander Beliavsky is a Slovenian (formerly Soviet) chess grandmaster renowned for his aggressive playing style, four USSR Championship titles, and contributions as a top-level competitor and author.
E2295193 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: Alexander Beliavsky | Statement: [Botvinnik chess school, notableStudent, Alexander Beliavsky]
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: Alexander Beliavsky
Triple: [Botvinnik chess school, notableStudent, Alexander Beliavsky]
Generated description
Alexander Beliavsky is a Slovenian (formerly Soviet) chess grandmaster renowned for his aggressive playing style, four USSR Championship titles, and contributions as a top-level competitor and author.

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_69efb51dfb048190ada79b745c33b363 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69f6434f9f888190bd43fde92cd729fe completed May 2, 2026, 6:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a7d1c79dc80819098dd60336ac7bf93 completed Aug. 13, 2026, 1:23 a.m.
NEDg Description generation batch_6a7d1cce9bec8190ab6ec344321f3314 completed Aug. 13, 2026, 1:24 a.m.
NED2 Entity disambiguation (via description) batch_6a7d1d24cce48190a36b7f4acec7f5f3 completed Aug. 13, 2026, 1:25 a.m.
Created at: April 27, 2026, 10:45 p.m.