Triple

T22608593
Position Surface form Disambiguated ID Type / Status
Subject Shanyavsky Moscow City People’s University E566630 entity
Predicate educationalInnovation P85017 FINISHED
Object non-degree public lecture system LITERAL 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: non-degree public lecture system | Statement: [Shanyavsky Moscow City People’s University, educationalInnovation, non-degree public lecture system]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: educationalInnovation
Context triple: [Shanyavsky Moscow City People’s University, educationalInnovation, non-degree public lecture system]
  • A. educationalImpact
    Indicates the effect or influence that one entity has on the learning, knowledge, or educational outcomes of another.
  • B. educationalApproach
    Indicates the method, strategy, or philosophy used to guide teaching and learning within an educational context.
  • C. educationInitiative chosen
    Indicates a relationship where an entity plans, supports, or carries out a program or action aimed at improving or promoting education.
  • D. educationalSector
    Indicates a relationship in which something is part of, associated with, or operates within the education or schooling domain.
  • E. educationTrend
    Indicates a pattern or direction of change over time in some aspect of education, such as participation, attainment, or performance.
  • F. None of above.

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_69e245884860819081046ce07d5872c4 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f167e86794819097e9c1ea83db52e6 completed April 29, 2026, 2:07 a.m.
PD Predicate disambiguation batch_69ee627be4248190889a88764624e174 completed April 26, 2026, 7:07 p.m.
Created at: April 17, 2026, 2:55 p.m.