Triple
T1391476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cihu, Daxi District, Taoyuan, Taiwan |
E29967
|
entity |
| Predicate | primaryWritingSystem |
P454
|
FINISHED |
| Object | Traditional Chinese |
—
|
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 | Statement: [Cihu, Daxi District, Taoyuan, Taiwan, primaryWritingSystem, Traditional Chinese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryWritingSystem Context triple: [Cihu, Daxi District, Taoyuan, Taiwan, primaryWritingSystem, Traditional Chinese]
-
A.
writingSystem
chosen
Indicates that one entity is the script or system of written symbols used to represent the language or content of another entity.
-
B.
writingSystemDevelopedFrom
Indicates that one writing system originated, evolved, or was derived from another earlier writing system.
-
C.
writingSystemClass
Indicates that one entity is classified as a type or category of writing system to which the other entity belongs.
-
D.
writingSystemFeatures
Indicates the specific structural or functional characteristics that define how a particular writing system represents language.
-
E.
isMostWidelyUsedWritingSystem
Indicates that the subject writing system is used by more people or in more contexts than any other writing system.
- 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_69a498dc92f8819094a1108f8ac90f43 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c35f7ab081909fe81dd475d6196f |
completed | March 1, 2026, 10:53 p.m. |
| PD | Predicate disambiguation | batch_69a4beffcf808190ab4cd0271257ce63 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.