Triple
T32180423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cha |
E821963
|
entity |
| Predicate | frequencyInKorea |
P133412
|
FINISHED |
| Object | relatively uncommon |
—
|
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: relatively uncommon | Statement: [Cha, frequencyInKorea, relatively uncommon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyInKorea Context triple: [Cha, frequencyInKorea, relatively uncommon]
-
A.
frequencyCategoryInKorea
chosen
Indicates how frequently something occurs or is observed within the context of Korea, typically grouped into predefined frequency categories.
-
B.
frequencyInNorthKorea
Indicates how often something occurs or is present within the context of North Korea.
-
C.
frequencyGivenBy
Indicates that the frequency of something is specified, determined, or provided by a particular source or entity.
-
D.
frequencyInUS
Indicates how often something occurs, appears, or is used within the United States.
-
E.
frequencyCategory
Indicates how often an action, event, or relationship occurs, typically by assigning it to a qualitative frequency level (e.g., rare, occasional, frequent).
- 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_69f3490755288190aee11740a34862f9 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6e02ba6b881908dfafc52d3b75f1c |
completed | May 3, 2026, 5:42 a.m. |
| PD | Predicate disambiguation | batch_69f6de09c2f481909f8b2545d3208c9f |
completed | May 3, 2026, 5:32 a.m. |
Created at: May 1, 2026, 12:34 a.m.