Triple
T33752283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dongkou Xiang |
E864874
|
entity |
| Predicate | typicalStandardWrittenForm |
P200730
|
FINISHED |
| Object | Standard Written Chinese |
E7177
|
NE 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: Standard Written Chinese | Statement: [Dongkou Xiang, typicalStandardWrittenForm, Standard Written Chinese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalStandardWrittenForm Context triple: [Dongkou Xiang, typicalStandardWrittenForm, Standard Written Chinese]
-
A.
writingSystemStandardized
Indicates that a writing system has been formally codified and regulated according to an accepted standard or set of rules.
-
B.
standardizedFormOf
Indicates that one entity is the normalized or officially standardized version of another, often consolidating variations or alternative forms into a single canonical form.
-
C.
hasTypicalWritingSystem
chosen
Indicates that an entity customarily uses a particular writing system as its standard or most common form of written representation.
-
D.
writtenForm
Indicates that one entity is the textual or orthographic representation (spelling or written version) of another entity.
-
E.
standardTextIn
Indicates that an entity is contained within or associated with a standard textual document or body of standard text.
- F. None of above.
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_69f3498c35f881909df279ae4270f831 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037e0953908190b2930b3c06a40129 |
completed | May 12, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a365c92c4cc8190a6b551705c3d66a6 |
completed | June 20, 2026, 9:25 a.m. |
| PD | Predicate disambiguation | batch_6a0379f6c3308190b954f7810214ceed |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:45 a.m.