Triple
T31527922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yap |
E804395
|
entity |
| Predicate | hasLikelyChineseCharacter |
P194475
|
FINISHED |
| Object | 叶 |
—
|
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: 叶 | Statement: [Yap, hasLikelyChineseCharacter, 叶]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLikelyChineseCharacter Context triple: [Yap, hasLikelyChineseCharacter, 叶]
-
A.
hasMultipleChineseCharacters
Indicates that the referenced item consists of more than one Chinese character.
-
B.
hasTraditionalCharacter
Indicates that an entity is associated with or represented by a traditional (non-simplified or historically established) written character form.
-
C.
canRepresentMultipleChineseCharacters
Indicates that a given form (such as a sound, syllable, or written unit) is capable of corresponding to more than one distinct Chinese character.
-
D.
usedChineseCharacters
Indicates that one entity employed or wrote using Chinese characters in relation to another entity or context.
-
E.
hasTraditionalEnglishCharacter
Indicates that something possesses qualities, features, or style typically associated with traditional English culture or heritage.
- 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_69f348cf839c81908657048402f7f97b |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fd76d1e5208190a6f26651492d1e3c |
completed | May 8, 2026, 5:38 a.m. |
| PD | Predicate disambiguation | batch_69fd702a226c81908edfda00f4be4130 |
completed | May 8, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69fd76d0d7608190b350336d6c18182d |
completed | May 8, 2026, 5:38 a.m. |
Created at: April 30, 2026, 9:59 p.m.