Triple
T20068954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles Yang |
E499682
|
entity |
| Predicate | hasWritten |
P2831
|
FINISHED |
| Object | Knowledge and Learning in Natural Language |
—
|
NE NERFINISHED |
How this triple was built (3 steps)
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.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Knowledge and Learning in Natural Language | Statement: [Charles Yang, hasWritten, Knowledge and Learning in Natural Language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Knowledge and Learning in Natural Language Context triple: [Charles Yang, hasWritten, Knowledge and Learning in Natural Language]
-
A.
Semantics and Cognition
Semantics and Cognition is a foundational book in cognitive linguistics that explores how meaning in language is structured and represented in the human mind.
-
B.
Foundations of Statistical Natural Language Processing
Foundations of Statistical Natural Language Processing is a seminal textbook that introduces the core probabilistic and machine learning methods used in modern natural language processing.
-
C.
Learning a Natural Language Interface with Neural Programmer
"Learning a Natural Language Interface with Neural Programmer" is a research paper that introduces a neural network-based system for translating natural language questions into executable programs to answer queries over structured data.
-
D.
Language, Logic, and Conceptual Structure
Language, Logic, and Conceptual Structure is a foundational work in cognitive science and linguistics that explores how language, logical form, and mental representation interact in the human mind.
-
E.
Exploring the Limits of Language Modeling
"Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Knowledge and Learning in Natural Language Target entity description: "Knowledge and Learning in Natural Language" is a scholarly work by linguist Charles Yang that explores how humans acquire linguistic knowledge and how learning mechanisms shape natural language structure.
-
A.
Semantics and Cognition
Semantics and Cognition is a foundational book in cognitive linguistics that explores how meaning in language is structured and represented in the human mind.
-
B.
Foundations of Statistical Natural Language Processing
Foundations of Statistical Natural Language Processing is a seminal textbook that introduces the core probabilistic and machine learning methods used in modern natural language processing.
-
C.
Learning a Natural Language Interface with Neural Programmer
"Learning a Natural Language Interface with Neural Programmer" is a research paper that introduces a neural network-based system for translating natural language questions into executable programs to answer queries over structured data.
-
D.
Language, Logic, and Conceptual Structure
Language, Logic, and Conceptual Structure is a foundational work in cognitive science and linguistics that explores how language, logical form, and mental representation interact in the human mind.
-
E.
Exploring the Limits of Language Modeling
"Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
- F. None of above. chosen
Provenance (2 batches)
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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e664365ad0819089103b00d1cf8c9f |
completed | April 20, 2026, 5:36 p.m. |
Created at: April 11, 2026, 3:39 p.m.