Triple

T19086779
Position Surface form Disambiguated ID Type / Status
Subject UNALM E467169 entity
Predicate shortName P43 FINISHED
Object UNALM NE NERFINISHED

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: UNALM | Statement: [UNALM, shortName, UNALM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: UNALM
Context triple: [UNALM, shortName, UNALM]
  • A. UNALM chosen
    UNALM is a leading Peruvian public university specializing in agricultural, environmental, and related sciences.
  • B. UNAMET
    UNAMET (United Nations Mission in East Timor) was a UN peacekeeping and electoral mission established in 1999 to organize and oversee the referendum on East Timor’s political status and support its transition toward independence.
  • C. UNLP
    UNLP is a major Argentine public university located in La Plata, recognized for its comprehensive academic programs and significant contributions to research and culture.
  • D. UdeA
    UdeA is a major public research university in Medellín, Colombia, recognized for its academic excellence and cultural and scientific contributions to the region.
  • E. UNAH
    UNAH is the main public higher education institution in Honduras, known for its comprehensive academic programs and central role in the country’s research and professional training.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dd05ac4c8190b1967d8f97f3fb2f completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e347ee288190a3e935ff89ca94aa completed April 20, 2026, 8:26 a.m.
Created at: April 10, 2026, 12:04 p.m.