Triple
T11003298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation |
E260052
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | natural language processing paper |
C28998
|
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: natural language processing paper Context triple: [Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, instanceOf, natural language processing paper]
-
A.
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.
-
B.
natural language processing technique
A natural language processing technique is a computational method or algorithm designed to enable computers to understand, interpret, generate, or manipulate human language in a meaningful way.
-
C.
natural language
Natural language is a human-developed system of spoken, written, or signed communication characterized by grammar, vocabulary, and usage conventions that evolve within a linguistic community.
-
D.
linguistics journal
A linguistics journal is a periodical publication that presents peer-reviewed research articles, reviews, and scholarly discussions on the scientific study of language and its structure, use, and development.
-
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. chosen
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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
Created at: April 8, 2026, 9:25 p.m.