Triple
T9050959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chữ Nôm |
E216880
|
entity |
| Predicate | usesCharacterTypes |
P8572
|
FINISHED |
| Object | borrowed 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: borrowed Chinese characters | Statement: [Chữ Nôm, usesCharacterTypes, borrowed Chinese characters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesCharacterTypes Context triple: [Chữ Nôm, usesCharacterTypes, borrowed Chinese characters]
-
A.
usesCharactersAs
Indicates that one entity employs or incorporates specific characters (such as letters, symbols, or glyphs) from another entity for its representation or functioning.
-
B.
usesCharacter
Indicates that one entity employs, incorporates, or relies on a particular character (such as a symbol, letter, or persona) in its form, function, or representation.
-
C.
hasTypicalCharacterType
Indicates that an entity is commonly associated with or exemplified by a particular type of character or persona.
-
D.
workCharacterType
Indicates that a work involves or features a character of a specified type or role.
-
E.
characterSetType
chosen
Indicates the type or category of character set associated with or used by an entity.
- 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_69ca83d362e88190ae44b4e4dc194209 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc6b54423081908d9fd985109e336a |
completed | April 1, 2026, 12:48 a.m. |
| PD | Predicate disambiguation | batch_69cc5ee566b081909e3cdaf551dbd0ec |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:10 p.m.