Triple
T5884310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hong Kong Government Cantonese Romanization |
E130822
|
entity |
| Predicate | usesFixedSpellingsForCommonSyllables |
P67259
|
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, usesFixedSpellingsForCommonSyllables, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesFixedSpellingsForCommonSyllables Context triple: [Hong Kong Government Cantonese Romanization, usesFixedSpellingsForCommonSyllables, yes]
-
A.
usesSyllables
Indicates that one entity forms, expresses, or analyzes something by employing syllables as its basic units.
-
B.
hasTypicalSpelling
Indicates that one form is the standard or commonly accepted spelling of another form.
-
C.
spellingStability
Indicates the degree to which the spelling of a word or term remains consistent over time or across different uses.
-
D.
usesStandardOrthographyOf
Indicates that one entity writes or represents language according to the standard orthographic system defined for another entity.
-
E.
sharesSpellingWith
Indicates that two entities have identical or substantially identical written forms (i.e., they are spelled the same way).
- 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.