Triple
T4726039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeffrey Pfeffer |
E104885
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management
"Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management" is a management book by Jeffrey Pfeffer that advocates using rigorous evidence and data-driven thinking to make better organizational and leadership decisions.
|
E463929
|
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: Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management | Statement: [Jeffrey Pfeffer, notableWork, Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management Context triple: [Jeffrey Pfeffer, notableWork, Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management]
-
A.
The Cost-Benefit Revolution
The Cost-Benefit Revolution is a book by legal scholar Cass Sunstein that argues for the central role of cost-benefit analysis in modern regulation and public policy.
-
B.
Nudge: Improving Decisions About Health, Wealth, and Happiness
"Nudge: Improving Decisions About Health, Wealth, and Happiness" is a popular behavioral economics book by Richard Thaler and Cass Sunstein that explains how subtle changes in choice architecture can steer people toward better decisions without restricting their freedom.
-
C.
"The Nature of the Firm"
"The Nature of the Firm" is a foundational 1937 economic essay by Ronald Coase that explains why firms exist and how transaction costs shape their size and structure.
-
D.
Toulmin model of argumentation
The Toulmin model of argumentation is a framework for analyzing and constructing arguments by breaking them into components such as claim, data, warrant, backing, qualifier, and rebuttal.
-
E.
Evidence-Based Policymaking framework
The Evidence-Based Policymaking framework is a structured approach to designing, implementing, and evaluating public policies using rigorous data analysis, research, and measurable outcomes to inform government decision-making.
- 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: Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management Triple: [Jeffrey Pfeffer, notableWork, Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management]
Generated description
"Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management" is a management book by Jeffrey Pfeffer that advocates using rigorous evidence and data-driven thinking to make better organizational and leadership decisions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management Target entity description: "Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management" is a management book by Jeffrey Pfeffer that advocates using rigorous evidence and data-driven thinking to make better organizational and leadership decisions.
-
A.
The Cost-Benefit Revolution
The Cost-Benefit Revolution is a book by legal scholar Cass Sunstein that argues for the central role of cost-benefit analysis in modern regulation and public policy.
-
B.
Nudge: Improving Decisions About Health, Wealth, and Happiness
"Nudge: Improving Decisions About Health, Wealth, and Happiness" is a popular behavioral economics book by Richard Thaler and Cass Sunstein that explains how subtle changes in choice architecture can steer people toward better decisions without restricting their freedom.
-
C.
"The Nature of the Firm"
"The Nature of the Firm" is a foundational 1937 economic essay by Ronald Coase that explains why firms exist and how transaction costs shape their size and structure.
-
D.
Toulmin model of argumentation
The Toulmin model of argumentation is a framework for analyzing and constructing arguments by breaking them into components such as claim, data, warrant, backing, qualifier, and rebuttal.
-
E.
Evidence-Based Policymaking framework
The Evidence-Based Policymaking framework is a structured approach to designing, implementing, and evaluating public policies using rigorous data analysis, research, and measurable outcomes to inform government decision-making.
- 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_69bd43ed84648190ae0b7ee8e8d00482 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6446b42081908e023979c9685730 |
completed | March 20, 2026, 3:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be109c67648190ba9bda7fc5cb3dd1 |
completed | March 21, 2026, 3:29 a.m. |
| NEDg | Description generation | batch_69be134fbc088190a2c7b3c3e7f00584 |
completed | March 21, 2026, 3:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be13e813e08190842ad5c2ad8e47cc |
completed | March 21, 2026, 3:43 a.m. |
Created at: March 20, 2026, 1:18 p.m.