Triple
T31992130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 晏 |
E816899
|
entity |
| Predicate | kangxiDictionaryIndex |
P135702
|
FINISHED |
| Object | radical 72 plus 6 strokes |
—
|
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: radical 72 plus 6 strokes | Statement: [晏, kangxiDictionaryIndex, radical 72 plus 6 strokes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: kangxiDictionaryIndex Context triple: [晏, kangxiDictionaryIndex, radical 72 plus 6 strokes]
-
A.
dictionaryIndexKangxi
chosen
Indicates that an entry is indexed or referenced according to its position in the Kangxi dictionary system.
-
B.
kangxiRadicalNumber
Indicates the specific Kangxi radical index number associated with a given Chinese character or component.
-
C.
kangxiRadicalName
Indicates the specific Kangxi radical name associated with a given Chinese character or radical index.
-
D.
semanticRadicalOfCharacter
Indicates that one element is the semantic radical (meaning-bearing component) of a given written character.
-
E.
componentKanji1Meaning
Indicates that the first kanji component of a character corresponds to a particular meaning or semantic value.
- 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_69f348f8002081909a3588758ba94afb |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b3b9ad2c8190ba59409cbd3da87f |
completed | May 3, 2026, 2:32 a.m. |
| PD | Predicate disambiguation | batch_69f6b151ad008190836c1bcdec503ce2 |
completed | May 3, 2026, 2:22 a.m. |
Created at: May 1, 2026, 12:13 a.m.