Triple

T497584
Position Surface form Disambiguated ID Type / Status
Subject Faculty of Law, University of Havana E10327 entity
Predicate roleInSociety P268 FINISHED
Object preparation of legal experts for Cuban state institutions 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: preparation of legal experts for Cuban state institutions | Statement: [Faculty of Law, University of Havana, roleInSociety, preparation of legal experts for Cuban state institutions]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: roleInSociety
Context triple: [Faculty of Law, University of Havana, roleInSociety, preparation of legal experts for Cuban state institutions]
  • A. roleInIndustry
    Indicates the specific function, position, or capacity an entity holds within a particular industry or sector.
  • B. roleInText
    Indicates that an entity participates in a text with a specific function or capacity (e.g., author, editor, character).
  • C. roleInvolves
    Indicates that a particular role includes or requires participation in a specified activity, responsibility, or function.
  • D. urbanRole
    Indicates the function, status, or role that an entity holds within an urban or city context.
  • E. role chosen
    Indicates the function, position, or responsibility that one entity holds in relation to another within a given context.
  • 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_69a2e847df8481909239ec08ccf1e376 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f1183e988190bce70932a9678134 completed Feb. 28, 2026, 1:43 p.m.
PD Predicate disambiguation batch_69a2edfa87cc8190a77c726a5a55b7d9 completed Feb. 28, 2026, 1:30 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.