Triple
T6248192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanja |
E139773
|
entity |
| Predicate | readingSystem |
P69310
|
FINISHED |
| Object | Sino-Korean readings |
—
|
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: Sino-Korean readings | Statement: [Hanja, readingSystem, Sino-Korean readings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: readingSystem Context triple: [Hanja, readingSystem, Sino-Korean readings]
-
A.
readingTechnology
Indicates a relationship where a reading activity involves or is carried out using a particular technology.
-
B.
readingFeature
Indicates that an entity possesses a characteristic, capability, or attribute specifically related to reading.
-
C.
readingAid
Indicates that one entity assists or facilitates another entity’s ability to read or engage in reading activities.
-
D.
reading
Indicates that an entity is engaged in the activity of interpreting and understanding written or printed material from another entity or source.
-
E.
containsReading
Indicates that one entity includes or encompasses a particular reading (such as a measurement, value, or interpretation) within it.
- 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_69c008b1c5088190ae6de2555fc05ad8 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0633a9a048190856d5247d3b28a2e |
completed | March 22, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69c056037bf88190a0a3fe7429345d0b |
completed | March 22, 2026, 8:50 p.m. |
| PDg | Predicate description generation | batch_69c056df95ac8190bc5efe050d3af864 |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:23 p.m.