Triple

T771821
Position Surface form Disambiguated ID Type / Status
Subject Unua Libro E16296 entity
Predicate subject P450 FINISHED
Object Esperanto E2739 NE 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: Esperanto | Statement: [Unua Libro, subject, Esperanto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Esperanto
Context triple: [Unua Libro, subject, Esperanto]
  • A. Esperanto chosen
    Esperanto is a constructed international auxiliary language created in the late 19th century to facilitate easy and politically neutral communication between speakers of different native languages.
  • B. Winaray
    Winaray is an Austronesian language spoken primarily in the Eastern Visayas region of the Philippines, particularly in Samar, northern Leyte, and nearby areas.
  • C. Romani language
    The Romani language is an Indo-Aryan language traditionally spoken by Romani communities across Europe and beyond, featuring numerous dialects influenced by the languages of the regions where its speakers live.
  • D. Estonian language
    The Estonian language is a Finno-Ugric language spoken primarily in Estonia, closely related to Finnish and known for its complex grammar and rich vowel system.
  • E. Krio language
    Krio is an English-based creole language spoken primarily in Sierra Leone, where it serves as a major lingua franca among diverse ethnic groups.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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.
NED1 Entity disambiguation (via context triple) batch_69a6733773588190885d03d714e21b37 completed March 3, 2026, 5:35 a.m.
Created at: March 1, 2026, 7:37 p.m.