Triple

T285409
Position Surface form Disambiguated ID Type / Status
Subject Latin E5875 entity
Predicate orthographicFeature P6520 FINISHED
Object use of macrons in pedagogical texts to mark vowel length 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: use of macrons in pedagogical texts to mark vowel length | Statement: [Latin, orthographicFeature, use of macrons in pedagogical texts to mark vowel length]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: orthographicFeature
Context triple: [Latin, orthographicFeature, use of macrons in pedagogical texts to mark vowel length]
  • A. linguisticFeature chosen
    Indicates a relationship where a linguistic property, pattern, or characteristic is attributed to or associated with a language-related entity (such as a word, phrase, or text).
  • B. hasOfficialOrthography
    Indicates that an entity has a formally recognized and standardized system for writing its language or name.
  • C. hasStandardOrthographySince
    Indicates that a language or writing system has used a particular standardized orthography starting from a specified point in time.
  • D. skeletonFeature
    Indicates that one entity is a structural or anatomical skeletal feature or component of another entity.
  • E. hasWritingDirection
    Indicates the direction in which writing or text is read or written for a given script, language, or text system.
  • 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_69a25946a7ac8190a78871c210213272 completed Feb. 28, 2026, 2:56 a.m.
NER Named-entity recognition batch_69a2605b372c8190831570aa6532cc96 completed Feb. 28, 2026, 3:26 a.m.
PD Predicate disambiguation batch_69a25b7a8d148190aacdcc8ccb35c7f3 completed Feb. 28, 2026, 3:05 a.m.
Created at: Feb. 28, 2026, 3:02 a.m.