Triple
T6066268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pyongyang Sunan International Airport |
E135167
|
entity |
| Predicate | hasOfficialLanguageForSignage |
P25263
|
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: [Pyongyang Sunan International Airport, hasOfficialLanguageForSignage, Korean]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOfficialLanguageForSignage Context triple: [Pyongyang Sunan International Airport, hasOfficialLanguageForSignage, Korean]
-
A.
officialLanguageOfSignage
chosen
Indicates that a particular language is the one officially used on public signs and signage within a given place or context.
-
B.
hasLanguageOfOfficialName
Indicates that an entity’s official name is expressed in a specified language.
-
C.
isUNOfficialLanguage
Indicates that a language holds official status within the United Nations.
-
D.
languageOfOfficialAnnouncements
Indicates the language used for formal or official public announcements issued by an authority.
-
E.
languageOfSignage
Indicates the language used on signs or written displays associated with an entity.
- 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_69c00878d06881909ee78e88913bf890 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0573df7508190bbb5b496188b2f3e |
completed | March 22, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69c049f031408190b08b2766237c5dd0 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:10 p.m.