Triple
T22431987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guangdong Romanization |
E554522
|
entity |
| Predicate | toneCategory |
P148164
|
FINISHED |
| Object | Cantonese lexical tones |
—
|
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: Cantonese lexical tones | Statement: [Guangdong Romanization, toneCategory, Cantonese lexical tones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: toneCategory Context triple: [Guangdong Romanization, toneCategory, Cantonese lexical tones]
-
A.
tone
Indicates the characteristic attitude or emotional quality expressed in how something is communicated or presented.
-
B.
inTonality
Indicates that something (such as a musical element, passage, or piece) is expressed, structured, or interpreted within a specific musical key or tonal framework.
-
C.
toneCount
Indicates the number of distinct tones or tonal elements associated with an entity or expression.
-
D.
voiceType
Indicates the specific vocal style, quality, or role associated with an entity’s voice in a given context.
-
E.
tonal
Indicates that one entity has a tone, pitch pattern, or tonal quality in relation to another (such as a language, sound, or musical element).
- F. None of above. chosen
Provenance (4 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_69e11e5010e48190ae1e9c9db9697637 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15a32139481909baaf9275f5e0257 |
completed | April 29, 2026, 1:09 a.m. |
| PD | Predicate disambiguation | batch_69e898a327948190beee5e168006a0a7 |
completed | April 22, 2026, 9:45 a.m. |
| PDg | Predicate description generation | batch_69e8aa39e3388190b659d59948ebf3e6 |
completed | April 22, 2026, 11 a.m. |
Created at: April 16, 2026, 8:47 p.m.