Triple
T262206
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | China |
E5561
|
entity |
| Predicate | regionalScript |
P454
|
FINISHED |
| Object | Traditional Chinese (Hong Kong and Macau) |
—
|
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: Traditional Chinese (Hong Kong and Macau) | Statement: [China, regionalScript, Traditional Chinese (Hong Kong and Macau)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionalScript Context triple: [China, regionalScript, Traditional Chinese (Hong Kong and Macau)]
-
A.
recognizedRegionalLanguage
Indicates that a language holds officially recognized status within a specific region or subnational jurisdiction.
-
B.
regionalDialect
Indicates that one entity uses or is associated with a dialect specific to a particular geographic region in relation to another entity.
-
C.
writingSystem
chosen
Indicates that one entity is the script or system of written symbols used to represent the language or content of another entity.
-
D.
culturalRegion
Indicates that an entity is located in, associated with, or belongs to a specific cultural region or cultural area.
-
E.
languageZone
Indicates the linguistic region or area in which a language is predominantly used or officially recognized.
- 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25e2aba74819093eddd8d820260c0 |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b6c968c819094fc903a3a377e15 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.