Triple
T31665242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yongbi eocheonga commentary |
E808106
|
entity |
| Predicate | languageOfWorkAnalyzed |
P8513
|
FINISHED |
| Object | Korean |
—
|
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 | Statement: [Yongbi eocheonga commentary, languageOfWorkAnalyzed, Korean]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfWorkAnalyzed Context triple: [Yongbi eocheonga commentary, languageOfWorkAnalyzed, Korean]
-
A.
languageOfWorkRecognized
Indicates that a work is officially recognized as being created or expressed in a particular language.
-
B.
languageOfExpression
chosen
Indicates that a particular language is used as the medium or form in which an expression (such as a text, utterance, or work) is realized.
-
C.
mainLanguageOf
Indicates that a specified language is the primary or dominant language used by a particular entity (such as a person, document, or organization).
-
D.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
-
E.
languageUse
Indicates the language or languages an entity uses for communication, expression, or interaction.
- 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_69f348dbeef4819080b446a7feb6340b |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69ff9361943c81909544203cbc998a69 |
completed | May 9, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69ff913138a08190b59bdc9d8d199eb3 |
completed | May 9, 2026, 7:55 p.m. |
Created at: April 30, 2026, 10:59 p.m.