Triple
T2312086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peng |
E51981
|
entity |
| Predicate | romanizationSystem |
P6517
|
FINISHED |
| Object | Hanyu 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: Hanyu Pinyin | Statement: [Peng, romanizationSystem, Hanyu Pinyin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanyu Pinyin Context triple: [Peng, romanizationSystem, Hanyu 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.
Pe̍h-ōe-jī
Pe̍h-ōe-jī is a Latin-based orthography developed by Western missionaries for writing Southern Min (Hokkien) and related Chinese dialects.
-
C.
Taiwanese Romanization System
The Taiwanese Romanization System is a standardized Latin-based orthography used to phonetically represent Taiwanese Hokkien.
-
D.
Mandarin Chinese
Mandarin Chinese is the most widely spoken variety of Chinese and a major world language used across mainland China, Taiwan, and many overseas Chinese communities.
-
E.
Hakka Romanization System
The Hakka Romanization System is a standardized method of writing the Hakka Chinese language using the Latin alphabet to represent its sounds and tones.
- 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_69a88b0bb30c81908ded03b006d29387 |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc61a8e248190b5024cca9efd806d |
completed | March 7, 2026, 6:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae895c95388190848f592fc5d48ec6 |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:49 p.m.