Triple
T18876925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edward Feigenbaum |
E461710
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Knowledge Engineering |
—
|
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 Engineering | Statement: [Edward Feigenbaum, notableWork, Knowledge Engineering]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Knowledge Engineering Context triple: [Edward Feigenbaum, notableWork, Knowledge Engineering]
-
A.
Intelligent Systems
Intelligent Systems is a Japanese video game development company best known for creating the Fire Emblem and Paper Mario series.
-
B.
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering is a leading peer-reviewed journal published by the IEEE Computer Society that focuses on research in knowledge discovery, data mining, databases, and data-intensive systems.
-
C.
NETL knowledge representation system
The NETL knowledge representation system is an AI framework developed by Scott Fahlman for representing and reasoning about natural language knowledge in a structured, machine-interpretable form.
-
D.
“A System for Representing and Using Real-World Knowledge”
“A System for Representing and Using Real-World Knowledge” is a seminal AI research paper by John McCarthy that introduces a logical framework for representing commonsense knowledge about the real world.
-
E.
Global Open Knowledgebase (GOKb) collaboration
The Global Open Knowledgebase (GOKb) collaboration is an international, community-driven initiative that provides open, curated metadata about electronic resources to support library and scholarly communication workflows.
- 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 Engineering Target entity description: Knowledge Engineering is a field in artificial intelligence focused on designing, building, and maintaining knowledge-based systems that capture and apply expert-level reasoning.
-
A.
Intelligent Systems
Intelligent Systems is a Japanese video game development company best known for creating the Fire Emblem and Paper Mario series.
-
B.
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering is a leading peer-reviewed journal published by the IEEE Computer Society that focuses on research in knowledge discovery, data mining, databases, and data-intensive systems.
-
C.
NETL knowledge representation system
The NETL knowledge representation system is an AI framework developed by Scott Fahlman for representing and reasoning about natural language knowledge in a structured, machine-interpretable form.
-
D.
“A System for Representing and Using Real-World Knowledge”
“A System for Representing and Using Real-World Knowledge” is a seminal AI research paper by John McCarthy that introduces a logical framework for representing commonsense knowledge about the real world.
-
E.
Global Open Knowledgebase (GOKb) collaboration
The Global Open Knowledgebase (GOKb) collaboration is an international, community-driven initiative that provides open, curated metadata about electronic resources to support library and scholarly communication workflows.
- 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_69d8dcfc3430819095ee6fc0eb4c06a5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c3cf0aa0819090991fc9e14910fb |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 10, 2026, 11:57 a.m.