Triple

T12026477
Position Surface form Disambiguated ID Type / Status
Subject Tao E286290 entity
Predicate romanizationSystem P6517 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: [Tao, romanizationSystem, Pinyin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pinyin
Context triple: [Tao, romanizationSystem, 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_69d6ab4669e48190b59246358b0383ab completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903f02638819091e0cc0e93fa5ea7 completed April 10, 2026, 2:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69f48b8111b88190a42a8904a2d26862 completed May 1, 2026, 11:16 a.m.
Created at: April 8, 2026, 9:47 p.m.