Triple

T3181391
Position Surface form Disambiguated ID Type / Status
Subject Pei E66594 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: [Pei, hasRomanizationSystem, pinyin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: pinyin
Context triple: [Pei, 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. Pinghua
    Pinghua is a Sinitic language variety spoken primarily in parts of Guangxi and neighboring regions in southern China, often considered distinct from both Cantonese and Mandarin.
  • 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_69ad8587c1bc8190a2595f2c22ee1001 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada6a1280c8190b59a2afd30312c02 completed March 8, 2026, 4:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69b24b777fe881909764e2f2cdb68479 completed March 12, 2026, 5:13 a.m.
Created at: March 8, 2026, 3:06 p.m.