Triple
T36704201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | word2vec |
E906310
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | distributional semantics model |
C65333
|
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: distributional semantics model Context triple: [word2vec, instanceOf, distributional semantics model]
-
A.
distributed representation model
chosen
A distributed representation model is a computational framework in which concepts, entities, or inputs are encoded as patterns of activity across many dimensions or units, allowing information to be represented in a shared, overlapping, and highly expressive vector space.
-
B.
natural language processing model
A natural language processing model is a computational system designed to understand, interpret, generate, and manipulate human language in a meaningful way.
-
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.
semantic framework
A semantic framework is a structured system of concepts, rules, and relationships used to define, interpret, and reason about meaning within a particular domain or language.
-
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_69f76e7195c48190b5580c9cfb01e95f |
completed | May 3, 2026, 3:49 p.m. |
Created at: May 3, 2026, 4:12 p.m.