Triple
T36487900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Latent Dirichlet Allocation |
E898981
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | bag-of-words 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: bag-of-words model Context triple: [Latent Dirichlet Allocation, instanceOf, bag-of-words 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.
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.
hierarchical transformer model
A hierarchical transformer model is a neural network architecture that processes data at multiple levels of granularity (e.g., tokens, sentences, documents) using stacked transformer layers to capture both local and global contextual dependencies efficiently.
-
D.
data classification and labeling service
A data classification and labeling service organizes raw data into predefined categories and applies accurate, consistent labels to enable effective analysis, model training, and information retrieval.
-
E.
natural language understanding platform
A natural language understanding platform is a system that interprets, analyzes, and derives meaning from human language input to enable intelligent, context-aware interactions and automation.
- 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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
Created at: May 3, 2026, 4:10 p.m.