Triple
T15459455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liu |
E371857
|
entity |
| Predicate | toneInMandarin |
P86299
|
FINISHED |
| Object | second 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: second tone | Statement: [Liu, toneInMandarin, second tone]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: toneInMandarin Context triple: [Liu, toneInMandarin, second tone]
-
A.
mandarinTone
chosen
Indicates the specific tonal pattern in Mandarin Chinese with which an entity (such as a syllable or word) is pronounced.
-
B.
mandarinReadingBopomofo
Indicates the Bopomofo (Zhuyin) phonetic transcription used to represent the Mandarin pronunciation of a given expression or character.
-
C.
hasMandarinReading
Indicates that an entity is associated with a specific reading or pronunciation in Mandarin Chinese.
-
D.
hasPhonemicTone
Indicates that a language, word, or syllable uses pitch differences (tones) as phonemic contrasts that can change meaning.
-
E.
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.
- 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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f1623f0819086f6fc2bfd536609 |
completed | April 16, 2026, 1:44 a.m. |
| PD | Predicate disambiguation | batch_69ded284bd008190b31c53b4f1cebadd |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:32 a.m.