Triple
T12812705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beihai Municipal People's Government |
E306310
|
entity |
| Predicate | writingSystemOfWork |
P26603
|
FINISHED |
| Object | Simplified 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: Simplified Chinese | Statement: [Beihai Municipal People's Government, writingSystemOfWork, Simplified Chinese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: writingSystemOfWork Context triple: [Beihai Municipal People's Government, writingSystemOfWork, Simplified Chinese]
-
A.
writingSystem
Indicates that one entity is the script or system of written symbols used to represent the language or content of another entity.
-
B.
writingSystemUsedIn
chosen
Indicates that a particular writing system is employed for written communication within a given language, region, or context.
-
C.
writingSystemClass
Indicates that one entity is classified as a type or category of writing system to which the other entity belongs.
-
D.
writingSystemCreator
Indicates that one entity is the creator or originator of the writing system used by another entity.
-
E.
writingSystemDevelopedFor
Indicates that a particular writing system was created or adapted specifically to be used for a given language, community, or purpose.
- 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e9adcf08190a12801adcc613477 |
completed | April 10, 2026, 9:41 p.m. |
| PD | Predicate disambiguation | batch_69d9640ed7448190b276e7fab649f7d2 |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:31 p.m.