Triple
T19701878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorasan Station |
E473116
|
entity |
| Predicate | languageOnSigns |
P4196
|
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: [Dorasan Station, languageOnSigns, Korean]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOnSigns Context triple: [Dorasan Station, languageOnSigns, Korean]
-
A.
officialLanguageOfSignage
Indicates that a particular language is the one officially used on public signs and signage within a given place or context.
-
B.
languageOfSignage
chosen
Indicates the language used on signs or written displays associated with an entity.
-
C.
languageOfSignatures
Indicates the language in which the signatures on a document or agreement are written or expressed.
-
D.
tertiaryLanguageOfSignage
Indicates that a language is used as the third-most prominent language on signage in a given context or location.
-
E.
languageOfLetters
Indicates that one entity is the language in which the other entity’s letters or written correspondence are composed.
- 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e642b667908190841bb5fb7bfdb3f7 |
completed | April 20, 2026, 3:13 p.m. |
| PD | Predicate disambiguation | batch_69e530438c60819082364c7be3eef6f0 |
completed | April 19, 2026, 7:42 p.m. |
Created at: April 10, 2026, 1:46 p.m.