Triple
T24076680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gim |
E596380
|
entity |
| Predicate | frequencyAsSurnameInKorea |
P122732
|
FINISHED |
| Object | very high |
—
|
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: very high | Statement: [Gim, frequencyAsSurnameInKorea, very high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyAsSurnameInKorea Context triple: [Gim, frequencyAsSurnameInKorea, very high]
-
A.
nameInKorean
Indicates that an entity’s name is expressed in the Korean language.
-
B.
hasSurnameFrequency
chosen
Indicates that a surname occurs with a specified frequency or rate within a given population or dataset.
-
C.
languageOfKoreanName
Indicates that the specified language is the language in which the given Korean name is expressed or written.
-
D.
frequencyCategoryInKorea
Indicates how frequently something occurs or is observed within the context of Korea, typically grouped into predefined frequency categories.
-
E.
hangulNameRomanized
Indicates that an entity’s Korean Hangul name is represented in its romanized (Latin alphabet) form.
- 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_69e288c3999c8190809b282a04813dec |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1db1e959c81909f4365b5d7f934d9 |
completed | April 29, 2026, 10:19 a.m. |
| PD | Predicate disambiguation | batch_69f1764b1d4c8190b12590c6339c31c1 |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 10:42 p.m.