Triple

T771845
Position Surface form Disambiguated ID Type / Status
Subject Unua Libro E16296 entity
Predicate licenseModelProposed P8460 FINISHED
Object royalty-free use of Esperanto 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: royalty-free use of Esperanto | Statement: [Unua Libro, licenseModelProposed, royalty-free use of Esperanto]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: licenseModelProposed
Context triple: [Unua Libro, licenseModelProposed, royalty-free use of Esperanto]
  • A. licenseModel chosen
    Indicates the licensing scheme or framework that governs how something may be used, distributed, or accessed.
  • B. licensePreference
    Indicates a party’s chosen or prioritized type of license to use, grant, or operate under in a given context.
  • C. licenseFor
    Indicates that one entity grants or holds formal permission or authorization for another entity to perform an activity, use a resource, or operate under specified conditions.
  • D. license
    Indicates that one entity has granted another entity formal permission or authorization to use, perform, or exploit something under specified terms.
  • E. licenseBuiltAs
    Indicates that one entity is constructed, configured, or deployed under the terms or identity of another entity’s license.
  • 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_69a49369a0848190af883934cee3db4c completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a706abf88190a1cbc2dfbbf9968a completed March 1, 2026, 8:52 p.m.
PD Predicate disambiguation batch_69a4a508c42c8190850a0ac7844a3ea9 completed March 1, 2026, 8:43 p.m.
Created at: March 1, 2026, 7:37 p.m.