Triple

T21903726
Position Surface form Disambiguated ID Type / Status
Subject ENAC E540875 entity
Predicate acronym P43 FINISHED
Object ENAC 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: ENAC | Statement: [ENAC, acronym, ENAC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ENAC
Context triple: [ENAC, acronym, ENAC]
  • A. ENAC chosen
    ENAC is the School of Architecture, Civil and Environmental Engineering at EPFL in Switzerland, encompassing education and research in the built and natural environment.
  • B. ÉTS
    ÉTS is a Montreal-based engineering school specializing in applied research and training highly skilled engineers for industry.
  • C. ENSTA Paris
    ENSTA Paris is a leading French grande école of engineering and research, specializing in advanced science and technology, and is one of the founding schools of the Institut Polytechnique de Paris.
  • D. ENSAM
    ENSAM is a prestigious French grande école of engineering known for its strong focus on mechanical and industrial engineering and its nationwide network of campuses.
  • E. HEC
    HEC is Pakistan’s apex regulatory and funding body responsible for overseeing, accrediting, and promoting higher education and research in the country.
  • 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_69e0c47b4e8c81908c8076eaa4c8e4f2 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f121d3c23081908c30c3a617002389 completed April 28, 2026, 9:08 p.m.
Created at: April 16, 2026, 7:26 p.m.