Triple
T5465193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donald W. Loveland |
E122690
|
entity |
| Predicate | authorOf |
P4244
|
FINISHED |
| Object |
"Logic for Computer Science: Foundations of Automatic Theorem Proving"
"Logic for Computer Science: Foundations of Automatic Theorem Proving" is a textbook that introduces the logical foundations and practical techniques underlying automated theorem proving and its applications in computer science.
|
E524533
|
NE FINISHED |
How this triple was built (4 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: "Logic for Computer Science: Foundations of Automatic Theorem Proving" | Statement: [Donald W. Loveland, authorOf, "Logic for Computer Science: Foundations of Automatic Theorem Proving"]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: "Logic for Computer Science: Foundations of Automatic Theorem Proving" Context triple: [Donald W. Loveland, authorOf, "Logic for Computer Science: Foundations of Automatic Theorem Proving"]
-
A.
First-Order Logic and Automated Theorem Proving
"First-Order Logic and Automated Theorem Proving" is a foundational textbook that systematically introduces first-order logic while presenting key methods and algorithms used in automated theorem proving.
-
B.
"Automated Theorem Proving: A Logical Basis"
"Automated Theorem Proving: A Logical Basis" is a foundational textbook that presents the logical theory and algorithms underlying automated reasoning and theorem-proving systems in computer science and mathematical logic.
-
C.
The Logic of Computer Programming
The Logic of Computer Programming is a foundational textbook in theoretical computer science that rigorously develops methods for specifying, proving, and reasoning about the correctness of computer programs.
-
D.
"The Complexity of Theorem-Proving Procedures"
"The Complexity of Theorem-Proving Procedures" is Stephen Cook’s landmark 1971 paper that introduced the concept of NP-completeness and proved the Boolean satisfiability problem (SAT) to be NP-complete, laying the foundation for modern computational complexity theory.
-
E.
Handbook of Automated Reasoning
The "Handbook of Automated Reasoning" is a comprehensive reference work that surveys the theories, methods, and tools used in the field of automated theorem proving and formal reasoning in computer science and logic.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: "Logic for Computer Science: Foundations of Automatic Theorem Proving" Triple: [Donald W. Loveland, authorOf, "Logic for Computer Science: Foundations of Automatic Theorem Proving"]
Generated description
"Logic for Computer Science: Foundations of Automatic Theorem Proving" is a textbook that introduces the logical foundations and practical techniques underlying automated theorem proving and its applications in computer science.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: "Logic for Computer Science: Foundations of Automatic Theorem Proving" Target entity description: "Logic for Computer Science: Foundations of Automatic Theorem Proving" is a textbook that introduces the logical foundations and practical techniques underlying automated theorem proving and its applications in computer science.
-
A.
First-Order Logic and Automated Theorem Proving
"First-Order Logic and Automated Theorem Proving" is a foundational textbook that systematically introduces first-order logic while presenting key methods and algorithms used in automated theorem proving.
-
B.
"Automated Theorem Proving: A Logical Basis"
"Automated Theorem Proving: A Logical Basis" is a foundational textbook that presents the logical theory and algorithms underlying automated reasoning and theorem-proving systems in computer science and mathematical logic.
-
C.
The Logic of Computer Programming
The Logic of Computer Programming is a foundational textbook in theoretical computer science that rigorously develops methods for specifying, proving, and reasoning about the correctness of computer programs.
-
D.
"The Complexity of Theorem-Proving Procedures"
"The Complexity of Theorem-Proving Procedures" is Stephen Cook’s landmark 1971 paper that introduced the concept of NP-completeness and proved the Boolean satisfiability problem (SAT) to be NP-complete, laying the foundation for modern computational complexity theory.
-
E.
Handbook of Automated Reasoning
The "Handbook of Automated Reasoning" is a comprehensive reference work that surveys the theories, methods, and tools used in the field of automated theorem proving and formal reasoning in computer science and logic.
- F. None of above. chosen
Provenance (5 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_69bd4643f16081908d7f29e08096115a |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd920590b481909b92091678ff1414 |
completed | March 20, 2026, 6:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf70d5dba4819086585fba83134f2b |
completed | March 22, 2026, 4:32 a.m. |
| NEDg | Description generation | batch_69bf73d994b08190866aae79c93d9393 |
completed | March 22, 2026, 4:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf743f2f348190aa6b44670166488a |
completed | March 22, 2026, 4:46 a.m. |
Created at: March 20, 2026, 2:08 p.m.