Triple
T19494378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ARC2 |
E487731
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | text classification model |
C25414
|
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: text classification model Context triple: [ARC2, instanceOf, text classification model]
-
A.
natural language processing model
chosen
A natural language processing model is a computational system designed to understand, interpret, generate, and manipulate human language in a meaningful way.
-
B.
social classification
Social classification is the systematic process of categorizing individuals or groups within a society based on attributes such as socioeconomic status, ethnicity, gender, occupation, or education, which shapes their access to resources, power, and opportunities.
-
C.
classification board
A classification board is an authoritative body or panel that evaluates and assigns categories, ratings, or classifications to items such as media, products, or information based on defined criteria and standards.
-
D.
machine learning model class
A machine learning model class is a blueprint that defines the structure, parameters, and learning behavior of models that can be instantiated to learn patterns from data and make predictions or decisions.
-
E.
statistical classification
Statistical classification is the process of assigning items or observations to predefined categories or classes based on their measured features using probabilistic or algorithmic decision rules.
- 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
Created at: April 10, 2026, 1:40 p.m.