Triple
T316193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khinkali |
E7711
|
entity |
| Predicate | typicalCourse |
P11940
|
FINISHED |
| Object | main course |
—
|
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: main course | Statement: [Khinkali, typicalCourse, main course]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCourse Context triple: [Khinkali, typicalCourse, main course]
-
A.
courseType
Indicates the classification or category of a course based on its nature, level, or instructional format.
-
B.
offersCourseType
Indicates that an entity provides or makes available a course of a specified type.
-
C.
taughtAs
Indicates that one entity served as a teacher or instructor for another entity in an educational or training context.
-
D.
studiedUnder
Indicates that one entity received instruction, training, or mentorship from another, typically in an academic or apprenticeship context.
-
E.
typicalTraining
Indicates that an entity commonly undergoes or is associated with a standard or usual form of training in relation to another entity or context.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea6462148190825acc57f6d2adaf |
completed | Feb. 28, 2026, 1:15 p.m. |
| PD | Predicate disambiguation | batch_69a2e943f12c8190883854aeed974260 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea08878c8190a5e8a90f620a3888 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.