Triple

T15803343
Position Surface form Disambiguated ID Type / Status
Subject Lei E383148 entity
Predicate hasRomanizationSystem P23170 FINISHED
Object Pinyin E175084 NE 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: Pinyin | Statement: [Lei, hasRomanizationSystem, Pinyin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pinyin
Context triple: [Lei, hasRomanizationSystem, Pinyin]
  • A. Hanyu Pinyin chosen
    Hanyu Pinyin is the official romanization system for Standard Mandarin Chinese, using the Latin alphabet to represent Chinese pronunciation.
  • B. Tongyong Pinyin
    Tongyong Pinyin is a romanization system for Mandarin Chinese that was once officially used in Taiwan as an alternative to Hanyu Pinyin.
  • C. Zhuyin
    Zhuyin is a phonetic writing system for transcribing the sounds of Mandarin Chinese, primarily used in Taiwan for teaching pronunciation and literacy.
  • D. Pe̍h-ōe-jī
    Pe̍h-ōe-jī is a Latin-based orthography developed by Western missionaries for writing Southern Min (Hokkien) and related Chinese dialects.
  • E. Jyutping
    Jyutping is a Cantonese romanization system developed by the Linguistic Society of Hong Kong that uses Latin letters and numbers to represent Cantonese pronunciation.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d86da2858c819090cc8481e7207b6e completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e0b524835c8190ae286b2562f07756 completed April 16, 2026, 10:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff90b4fe5881909471887219654d69 completed May 9, 2026, 7:53 p.m.
Created at: April 10, 2026, 4:48 a.m.