Triple

T21868910
Position Surface form Disambiguated ID Type / Status
Subject Braille E539952 entity
Predicate hasVariant P455 FINISHED
Object Japanese Braille 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: Japanese Braille | Statement: [Braille, hasVariant, Japanese Braille]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Japanese Braille
Context triple: [Braille, hasVariant, Japanese Braille]
  • A. Korean Braille
    Korean Braille is the tactile writing system for the Korean language, designed to represent Hangul syllables in a format readable by touch for blind and visually impaired users.
  • B. Braille chosen
    Braille is a tactile writing system using raised dots that enables blind and visually impaired people to read and write through touch.
  • C. Hiragana
    Hiragana is a Japanese phonetic syllabary used primarily for native words, grammatical elements, and beginners’ reading and writing.
  • D. Bugis script
    Bugis script is a traditional Brahmic writing system used primarily for the Buginese language of South Sulawesi, Indonesia.
  • E. Kanji
    Kanji are logographic characters of Chinese origin used in the Japanese writing system alongside hiragana and katakana.
  • 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_69e0c478f59081909d54302b57fc1ce3 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f0f33305d081908cd070134420607a completed April 28, 2026, 5:49 p.m.
Created at: April 16, 2026, 6:57 p.m.