Triple
T7724152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ma |
E175086
|
entity |
| Predicate | pinyinToneForms |
P41219
|
FINISHED |
| Object | mā |
—
|
LITERAL 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: mā | Statement: [Ma, pinyinToneForms, mā]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pinyinToneForms Context triple: [Ma, pinyinToneForms, mā]
-
A.
hasPhonemicTone
Indicates that a language, word, or syllable uses pitch differences (tones) as phonemic contrasts that can change meaning.
-
B.
ChinesePinyin
chosen
Indicates that one entity is the Chinese pinyin (romanized phonetic transcription) representation of another entity.
-
C.
mandarinReadingBopomofo
Indicates the Bopomofo (Zhuyin) phonetic transcription used to represent the Mandarin pronunciation of a given expression or character.
-
D.
cantoneseJyutping
Indicates that an entity’s name or term is represented using the Cantonese Jyutping romanization system.
-
E.
usesToneMarks
Indicates that one entity applies or includes diacritical tone marks in the representation or transcription of another entity (such as text, language, or symbols).
- F. None of above.
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_69c6995d541c81909eaa646b1a8369a9 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7074eca4c8190bd51fd1b450729e8 |
completed | March 27, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69c7016a6cf88190b53bf4b958f0f302 |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:05 p.m.