Triple
T26766168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Outline of Knowledge |
E674946
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | knowledge classification system |
C14542
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: knowledge classification system Context triple: [Outline of Knowledge, instanceOf, knowledge classification system]
-
A.
statistical classification system
A statistical classification system is a structured framework that organizes data, entities, or phenomena into predefined categories based on quantitative criteria and statistical methods to enable consistent analysis and comparison.
-
B.
textual classification system
A textual classification system is a software component that automatically assigns predefined categories or labels to text inputs based on their content using rule-based, statistical, or machine learning methods.
-
C.
goods classification system
A goods classification system is a structured framework for categorizing products based on shared characteristics, such as type, use, material, or regulatory requirements, to enable consistent identification, management, and analysis.
-
D.
canonical classification
Canonical classification is a standardized method of organizing entities into universally recognized categories based on their essential, defining characteristics.
-
E.
bibliographic classification
chosen
A bibliographic classification is a systematic scheme for organizing library and information resources into categories and subcategories to facilitate efficient retrieval and browsing.
- F. None of above.
Provenance (1 batch)
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_69eecda85298819097ee1c38a3d772e7 |
completed | April 27, 2026, 2:44 a.m. |
Created at: April 27, 2026, 4 a.m.