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.