Triple

T2962395
Position Surface form Disambiguated ID Type / Status
Subject Universal Esperanto Association E80078 entity
Predicate shortName P43 FINISHED
Object UEA E79014 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: UEA | Statement: [Universal Esperanto Association, shortName, UEA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: UEA
Context triple: [Universal Esperanto Association, shortName, UEA]
  • A. UEA chosen
    UEA is the World Esperanto Association, the leading international organization dedicated to promoting and coordinating the global Esperanto movement.
  • B. UCA
    UCA is the Unicode Collation Algorithm, a Unicode standard that defines a language-independent method for ordering and comparing Unicode text.
  • C. UNA
    UNA is the stock ticker symbol for Unilever, a major multinational consumer goods company known for its wide range of food, personal care, and household products.
  • D. UoC
    UoC is a major public research university in Cologne, Germany, known for its broad range of academic disciplines and large student population.
  • E. University of East Anglia
    The University of East Anglia is a public research university in Norwich, England, renowned for its strengths in environmental sciences, creative writing, and interdisciplinary research.
  • 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_69ad8b1341848190bd19dbf46892887d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9955e6488190bea170724d5fbfe8 completed March 8, 2026, 3:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0fc959b3c8190a0d95a3e616246f9 completed March 11, 2026, 5:24 a.m.
Created at: March 8, 2026, 2:57 p.m.