Triple
T32903180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Korean Cultural Center in Berlin |
E841664
|
entity |
| Predicate | hasLanguageCourse |
P5462
|
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: [Korean Cultural Center in Berlin, hasLanguageCourse, Korean language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageCourse Context triple: [Korean Cultural Center in Berlin, hasLanguageCourse, Korean language]
-
A.
hasLanguageOfStudy
chosen
Indicates that an entity studies or is engaged in learning a particular language.
-
B.
hasTypicalCourse
Indicates that there is a characteristic or commonly observed progression, sequence, or development pattern associated with the subject.
-
C.
languageBranchStudied
Indicates that a person studies or has studied a particular branch or subgroup of a language.
-
D.
alsoUsesLanguageOfInstruction
Indicates that an entity, in addition to its primary language, uses the same language that is designated as the language of instruction in a given context.
-
E.
hasCourseThrough
Indicates that something (such as a path, route, or flow) passes through or traverses another entity or area.
- 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_69f34946a5208190bbd79f0fec4323bd |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fecd0a732c819097bdd3eb69b6158c |
completed | May 9, 2026, 5:58 a.m. |
| PD | Predicate disambiguation | batch_69fecc0318d481908b5b20598a76a9fe |
completed | May 9, 2026, 5:54 a.m. |
Created at: May 1, 2026, 1:19 a.m.