Triple
T12088691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shu |
E287878
|
entity |
| Predicate | canRepresentMultipleChineseCharacters |
P103433
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Shu, canRepresentMultipleChineseCharacters, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canRepresentMultipleChineseCharacters Context triple: [Shu, canRepresentMultipleChineseCharacters, true]
-
A.
hasMultipleChineseCharacters
Indicates that the referenced item consists of more than one Chinese character.
-
B.
correspondsToChineseCharacter
Indicates that one entity is the equivalent or representation of a specific Chinese written character.
-
C.
hasTraditionalCharacter
Indicates that an entity is associated with or represented by a traditional (non-simplified or historically established) written character form.
-
D.
canBeWrittenWithMultipleKanji
Indicates that the same word or expression can be represented using more than one distinct kanji spelling.
-
E.
hasUnicode
Indicates that an entity is associated with, represented by, or encoded using a specific Unicode character or sequence.
- F. None of above. chosen
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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9178ad99c8190a54777b9bbe998bc |
completed | April 10, 2026, 3:30 p.m. |
| PD | Predicate disambiguation | batch_69d915000454819089fee00022055599 |
completed | April 10, 2026, 3:19 p.m. |
| PDg | Predicate description generation | batch_69d9178814e081908f67e3846718530e |
completed | April 10, 2026, 3:30 p.m. |
Created at: April 8, 2026, 9:48 p.m.