Triple

T22044962
Position Surface form Disambiguated ID Type / Status
Subject European Standards E544736 entity
Predicate nationalImplementationPrefix P7276 FINISHED
Object UNE-EN 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: UNE-EN | Statement: [European Standards, nationalImplementationPrefix, UNE-EN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: UNE-EN
Context triple: [European Standards, nationalImplementationPrefix, UNE-EN]
  • A. EUN
    EUN was the Olympic country code for the Unified Team of former Soviet republics that competed together at the 1992 Winter and Summer Olympics.
  • B. European Standards (EN) chosen
    European Standards (EN) are harmonized technical specifications adopted by European standardization bodies to ensure interoperability, safety, and quality across products and services within the European market.
  • C. CEN
    CEN (European Committee for Standardization) is a major European standards organization that develops and maintains voluntary technical standards to support trade, safety, and interoperability across Europe.
  • D. CEN
    CEN is a cloud networking service that connects and optimizes communication between distributed enterprise resources across regions and data centers.
  • E. CEN
    CEN is the station code for Hong Kong’s Central MTR station, a major interchange hub on the city’s rapid transit network.
  • 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_69e11e32445c8190ab97089b48a130bb completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f1282e647481908e054b3ad19e2c15 completed April 28, 2026, 9:35 p.m.
Created at: April 16, 2026, 8:25 p.m.