Triple
T34383111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeong |
E882487
|
entity |
| Predicate | requiresHanjaRegistrationInSouthKorea |
P199937
|
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: [Jeong, requiresHanjaRegistrationInSouthKorea, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: requiresHanjaRegistrationInSouthKorea Context triple: [Jeong, requiresHanjaRegistrationInSouthKorea, true]
-
A.
hasHangulName
Indicates that an entity is associated with a name written in the Korean Hangul script.
-
B.
hangulProjectCountry
Indicates that a country is associated with or involved in a Hangul-related project.
-
C.
hanjaName
Indicates that one entity is the Sino-Korean (hanja) written form corresponding to the name of another entity.
-
D.
hasHakkaRomanization
Indicates that an entity is associated with a specific representation of its name or term in Hakka Romanization.
-
E.
usesHanjaVariants
Indicates that one entity employs or incorporates alternative Hanja (Chinese character) forms corresponding to another entity.
- 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_69f349c0219881909393bbbc1edc8161 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff64b957bc81908afbc5914234a8ea |
completed | May 9, 2026, 4:45 p.m. |
| PD | Predicate disambiguation | batch_69ff6446593c81909173e296eea2590c |
completed | May 9, 2026, 4:43 p.m. |
| PDg | Predicate description generation | batch_69ff64b864f481909fcefc2b08595c89 |
completed | May 9, 2026, 4:45 p.m. |
Created at: May 1, 2026, 1:59 a.m.