Triple
T8903743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 川 |
E211993
|
entity |
| Predicate | mandarinTone |
P86299
|
FINISHED |
| Object | first tone |
—
|
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: first tone | Statement: [川, mandarinTone, first tone]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mandarinTone Context triple: [川, mandarinTone, first tone]
-
A.
hasPhonemicTone
Indicates that a language, word, or syllable uses pitch differences (tones) as phonemic contrasts that can change meaning.
-
B.
tonalCharacteristic
Indicates the specific quality or character of a sound’s tone, such as its color, texture, or expressive nuance, in relation to an entity.
-
C.
mandarinReadingBopomofo
Indicates the Bopomofo (Zhuyin) phonetic transcription used to represent the Mandarin pronunciation of a given expression or character.
-
D.
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).
-
E.
hasTonalityShift
Indicates a change in the tonal quality, mood, or key within a piece or segment, marking a shift from one tonality to another.
- 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_69ca839255248190b43984294abd92ae |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc64c2509881908fb692522d348e96 |
completed | April 1, 2026, 12:20 a.m. |
| PD | Predicate disambiguation | batch_69cc5ecf55248190a29f00fbf99f13c4 |
completed | March 31, 2026, 11:54 p.m. |
| PDg | Predicate description generation | batch_69cc604965c48190bbb6db0ae8108e67 |
completed | April 1, 2026, 12:01 a.m. |
Created at: March 30, 2026, 6:55 p.m.