Triple
T27846970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Youn |
E703850
|
entity |
| Predicate | transcribedFrom |
P182748
|
FINISHED |
| Object | Korean language |
—
|
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: Korean language | Statement: [Youn, transcribedFrom, Korean language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: transcribedFrom Context triple: [Youn, transcribedFrom, Korean language]
-
A.
scriptTranscribedFrom
Indicates that a written script is a transcription derived from another source, such as audio, video, or a different script.
-
B.
isTranscriptionOf
Indicates that one entity is a written, typed, or otherwise recorded representation of the content of another entity, preserving its original form as closely as possible.
-
C.
alsoTranscribes
Indicates that an entity, in addition to its primary transcription role, transcribes another specified entity as well.
-
D.
isTranscribedIn
Indicates that the genetic information of an entity (such as a gene or DNA region) is copied into an RNA molecule within a specified context (such as a cell type, tissue, or condition).
-
E.
transcribedAs
Indicates that one entity is a written or recorded representation of another entity’s content, typically converting it from one form (e.g., audio, handwriting) into text.
- 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_69ef840d9e3c819093615ebff4ec22be |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69f7908ec35881909a42f954fb9fa16e |
completed | May 3, 2026, 6:14 p.m. |
| PD | Predicate disambiguation | batch_69f78e2ac3fc819081a45c6841375c8d |
completed | May 3, 2026, 6:04 p.m. |
| PDg | Predicate description generation | batch_69f78fd3fd888190b7db0b563f298585 |
completed | May 3, 2026, 6:11 p.m. |
Created at: April 27, 2026, 6:08 p.m.