Triple
T24853585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lúzhōu |
E621957
|
entity |
| Predicate | representsTonePattern |
P163813
|
FINISHED |
| Object | secondToneOnLu |
—
|
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: secondToneOnLu | Statement: [Lúzhōu, representsTonePattern, secondToneOnLu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: representsTonePattern Context triple: [Lúzhōu, representsTonePattern, secondToneOnLu]
-
A.
representsNumberOfTones
Indicates that one entity specifies or encodes the number of tones associated with another entity.
-
B.
hasToneFunction
Indicates that one entity serves a specific tonal or harmonic function in relation to another entity within a musical context.
-
C.
romanizesTonePattern
Indicates that one entity converts the tonal pattern of another entity’s language or script into a romanized (Latin alphabet) representation.
-
D.
supportsTone
Indicates that one entity is compatible with, enables, or can correctly handle a specified tone or tonal characteristic of another entity.
-
E.
toneCount
Indicates the number of distinct tones or tonal elements associated with an entity or expression.
- 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_69e2fac297e481909d3aedc75f585e42 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f6416fbf4081909b0913c337927fc4 |
completed | May 2, 2026, 6:24 p.m. |
| PD | Predicate disambiguation | batch_69f63c6456608190b94e7c2e2c2a4824 |
completed | May 2, 2026, 6:03 p.m. |
| PDg | Predicate description generation | batch_69f63fd4f7448190930c723ba7cfce62 |
completed | May 2, 2026, 6:17 p.m. |
Created at: April 18, 2026, 5:21 a.m.