Triple
T5884312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hong Kong Government Cantonese Romanization |
E130822
|
entity |
| Predicate | isToneNeutral |
P67260
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Hong Kong Government Cantonese Romanization, isToneNeutral, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isToneNeutral Context triple: [Hong Kong Government Cantonese Romanization, isToneNeutral, yes]
-
A.
isNeutralAbout
Indicates that one entity has no strong positive or negative opinion, preference, or stance toward another entity or subject.
-
B.
isNeutralUnder
Indicates that one entity does not affect, alter, or interact with another in a way that changes its state, value, or behavior.
-
C.
usesNeutral
Indicates that one entity employs or applies something in a neutral, unbiased, or non-aligned manner toward another entity or context.
-
D.
contributesToTone
Indicates that one entity plays a role in shaping, influencing, or determining the overall tone or mood of another entity.
-
E.
neutralized
Indicates that one entity has rendered another entity ineffective, harmless, or no longer able to exert its intended effect.
- 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_69c0085628dc8190b334c1b44c067efc |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03fe07b7081909f8577ec3a9a1a8d |
completed | March 22, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69c0334bdc308190ad0d7199ab975588 |
completed | March 22, 2026, 6:22 p.m. |
| PDg | Predicate description generation | batch_69c03fdf954c8190ae97a5c9ce40bdfa |
completed | March 22, 2026, 7:15 p.m. |
Created at: March 22, 2026, 3:57 p.m.