Triple
T24495819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 부산과학기술대학교 |
E617786
|
entity |
| Predicate | 교육분야 |
P126649
|
FINISHED |
| Object | 공학 |
—
|
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: 공학 | Statement: [부산과학기술대학교, 교육분야, 공학]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 교육분야 Context triple: [부산과학기술대학교, 교육분야, 공학]
-
A.
educationalSector
Indicates a relationship in which something is part of, associated with, or operates within the education or schooling domain.
-
B.
educationalField
chosen
Indicates the academic or disciplinary area in which an educational activity, program, or qualification is focused.
-
C.
educationAndCulture
Indicates a relationship where activities, policies, or influences connect educational processes with cultural development, preservation, or expression.
-
D.
educationField
Indicates the academic or professional discipline in which an entity has been educated or trained.
-
E.
educationCategory
Indicates the classification of an educational program, content, or activity into a specific type or category based on its subject, level, or purpose.
- 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_69e2d7f4e6bc8190aec540ae3b9ed7f2 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2a9d912e88190bc39c05a9d7f407e |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6a4580481908fddc385f5262f95 |
completed | April 30, 2026, 12:47 a.m. |
Created at: April 18, 2026, 2:22 a.m.