Triple
T9610567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeong |
E232087
|
entity |
| Predicate | hasHanjaVariants |
P27905
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Jeong, hasHanjaVariants, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHanjaVariants Context triple: [Jeong, hasHanjaVariants, yes]
-
A.
hanjaName
chosen
Indicates that one entity is the Sino-Korean (hanja) written form corresponding to the name of another entity.
-
B.
hasHakkaRomanization
Indicates that an entity is associated with a specific representation of its name or term in Hakka Romanization.
-
C.
hasVariantReadingsWith
Indicates a relationship where two textual items are linked because they exhibit differing or alternative readings of (typically) the same underlying content.
-
D.
hasTraditionalCharacter
Indicates that an entity is associated with or represented by a traditional (non-simplified or historically established) written character form.
-
E.
hasJapaneseText
Indicates that an entity contains or is associated with text written in the Japanese language.
- 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_69ca8485a90c819094fe40b42fde9d70 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a85d4c881909ccab2e972d97e68 |
completed | April 1, 2026, 10:21 p.m. |
| PD | Predicate disambiguation | batch_69ccd5a6fd2481908efd131e207b8143 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:08 p.m.