Triple

T1306115
Position Surface form Disambiguated ID Type / Status
Subject LRSM diploma E27881 entity
Predicate languageOfExamination P10520 FINISHED
Object English LITERAL 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: English | Statement: [LRSM diploma, languageOfExamination, English]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: languageOfExamination
Context triple: [LRSM diploma, languageOfExamination, English]
  • A. languageOfAssessment chosen
    Indicates that a specified language is used as the medium of assessment (e.g., for tests, evaluations, or examinations) for a given entity.
  • B. languageOfExpression
    Indicates that a particular language is used as the medium or form in which an expression (such as a text, utterance, or work) is realized.
  • C. isLanguageOf
    Indicates that a particular language is used as the official or primary language associated with a given entity (such as a person, document, or region).
  • D. standardLanguageOf
    Indicates that one entity serves as the officially recognized or commonly used standard language for another entity (such as a country, region, or organization).
  • E. officialLanguage
    Indicates that a particular language has been formally designated by an authority as the official language used for government, legal, or administrative purposes in a given jurisdiction.
  • F. None of above.

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_69a496d7d83481908f83085854e51328 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c15490a88190872c3d2698a8f9c9 completed March 1, 2026, 10:44 p.m.
PD Predicate disambiguation batch_69a4bee9e4a88190b22ab2ee831a23c9 completed March 1, 2026, 10:34 p.m.
Created at: March 1, 2026, 7:51 p.m.