Triple
T7724054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanyu Pinyin |
E175084
|
entity |
| Predicate | alsoMarksTone |
P67251
|
FINISHED |
| Object | neutral 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: neutral tone | Statement: [Hanyu Pinyin, alsoMarksTone, neutral tone]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alsoMarksTone Context triple: [Hanyu Pinyin, alsoMarksTone, neutral tone]
-
A.
marksTones
chosen
Indicates that one entity applies or denotes tonal markings or distinctions on another entity, such as in language or notation.
-
B.
contributesToTone
Indicates that one entity plays a role in shaping, influencing, or determining the overall tone or mood of another entity.
-
C.
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).
-
D.
toneMarkFunction
Indicates a function or role that assigns, modifies, or interprets tone marks in a tonal or phonetic system.
-
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.
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.