Triple

T3825710
Position Surface form Disambiguated ID Type / Status
Subject Flying University E88683 entity
Predicate reasonForIllegality P25587 FINISHED
Object ban on higher education in Polish 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: ban on higher education in Polish | Statement: [Flying University, reasonForIllegality, ban on higher education in Polish]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: reasonForIllegality
Context triple: [Flying University, reasonForIllegality, ban on higher education in Polish]
  • A. statedReason
    Indicates that one entity expresses or provides another entity as the explanation, justification, or motive for an action, event, or claim.
  • B. reasonForBan chosen
    Indicates the justification or cause that led to an entity being banned.
  • C. legalConstraint
    Indicates that one entity imposes or is subject to a rule, restriction, or requirement defined by a legal or regulatory framework in relation to another entity or action.
  • D. reasonForConviction
    Indicates the specific offense or legal basis for which an individual was found guilty or convicted.
  • E. reasonForDisqualification
    Indicates the specific cause or justification for which an entity was deemed ineligible or disqualified from consideration or participation.
  • 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_69aed9538cf881909d9ce8ca4ac7c18c completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeeb8459f881908a2c91bb07e381ef completed March 9, 2026, 3:47 p.m.
PD Predicate disambiguation batch_69aee74c2e04819094b94b3c0bac1806 completed March 9, 2026, 3:29 p.m.
Created at: March 9, 2026, 3:17 p.m.