Triple
T27040015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hui Muslims |
E684460
|
entity |
| Predicate | commonScript |
P70071
|
FINISHED |
| Object | Chinese characters |
—
|
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: Chinese characters | Statement: [Hui Muslims, commonScript, Chinese characters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonScript Context triple: [Hui Muslims, commonScript, Chinese characters]
-
A.
commonApplication
Indicates that multiple entities share or participate in the same application, process, or usage context.
-
B.
primaryScript
Indicates the writing system or script that is chiefly used to represent the language or content of an entity.
-
C.
commonOn
Indicates that two or more entities share the same location, context, or medium where they are present or occur together.
-
D.
typicalScriptForm
chosen
Indicates the usual or standard written script or notation in which something is commonly represented.
-
E.
commonIn
Indicates that something frequently occurs, appears, or is found within a specified context, group, or environment.
- 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_69ef148193c48190bb1a0cfae6a407c4 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f71422adac8190a5ceb32dcf820833 |
completed | May 3, 2026, 9:23 a.m. |
| PD | Predicate disambiguation | batch_69f712764d2c819081b64b27e5de4a13 |
completed | May 3, 2026, 9:16 a.m. |
Created at: April 27, 2026, 8:04 a.m.