Triple

T2663488
Position Surface form Disambiguated ID Type / Status
Subject Faculty of Economics, Universidad de los Andes E54777 entity
Predicate offersTrainingFor P40765 FINISHED
Object economic policymakers 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: economic policymakers | Statement: [Faculty of Economics, Universidad de los Andes, offersTrainingFor, economic policymakers]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: offersTrainingFor
Context triple: [Faculty of Economics, Universidad de los Andes, offersTrainingFor, economic policymakers]
  • A. providesTrainingFor chosen
    Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
  • B. offersEducationTo
    Indicates that one entity provides educational services, instruction, or learning opportunities to another entity.
  • C. offersApprenticeshipTraining
    Indicates that one entity provides apprenticeship-based training opportunities or programs to another entity.
  • D. offersDiscipline
    Indicates that one entity provides or makes available a particular field of study, training, or area of specialization to another entity.
  • E. offersCertificationPreparationFor
    Indicates that an entity provides training or resources specifically designed to prepare individuals for obtaining a particular certification.
  • 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_69ab49e028948190b97e01d73548b1d9 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd96b9f1c8190a8a9460ca88a9aaf completed March 7, 2026, 7:53 a.m.
PD Predicate disambiguation batch_69abd81768748190bd965f367cf6ef37 completed March 7, 2026, 7:47 a.m.
Created at: March 6, 2026, 9:54 p.m.