Triple

T4445679
Position Surface form Disambiguated ID Type / Status
Subject Vivek Ranadivé E96278 entity
Predicate notableWork P4 FINISHED
Object The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities, and Beat the Competition
"The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities, and Beat the Competition" is a business and technology book by Vivek Ranadivé that explains how companies can use real-time data and predictive analytics to gain a competitive edge.
E438995 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: The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities, and Beat the Competition | Statement: [Vivek Ranadivé, notableWork, The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities, and Beat the Competition]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities, and Beat the Competition
Context triple: [Vivek Ranadivé, notableWork, The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities, and Beat the Competition]
  • A. The Future of Data Analysis
    "The Future of Data Analysis" is a seminal 1962 paper by statistician John W. Tukey that helped define and popularize exploratory data analysis and reshaped modern statistical practice.
  • B. Wharton Customer Analytics
    Wharton Customer Analytics is a research and education center at the Wharton School focused on advancing data-driven customer analytics through academic-industry collaboration.
  • C. Perception, Opportunity and Profit
    Perception, Opportunity and Profit is a seminal work in Austrian economics by Israel Kirzner that analyzes the role of entrepreneurial discovery in market processes and profit generation.
  • D. Experience and Prediction
    Experience and Prediction is a seminal philosophical work by Hans Reichenbach that develops a logical and probabilistic foundation for scientific knowledge and induction within the framework of logical empiricism.
  • E. The Decline and Rise of the Consumer
    "The Decline and Rise of the Consumer" is a work by philosopher and cultural pluralism advocate Horace M. Kallen that examines the role, power, and rights of consumers within modern industrial and democratic society.
  • 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: The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities, and Beat the Competition
Triple: [Vivek Ranadivé, notableWork, The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities, and Beat the Competition]
Generated description
"The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities, and Beat the Competition" is a business and technology book by Vivek Ranadivé that explains how companies can use real-time data and predictive analytics to gain a competitive edge.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities, and Beat the Competition
Target entity description: "The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities, and Beat the Competition" is a business and technology book by Vivek Ranadivé that explains how companies can use real-time data and predictive analytics to gain a competitive edge.
  • A. The Future of Data Analysis
    "The Future of Data Analysis" is a seminal 1962 paper by statistician John W. Tukey that helped define and popularize exploratory data analysis and reshaped modern statistical practice.
  • B. Wharton Customer Analytics
    Wharton Customer Analytics is a research and education center at the Wharton School focused on advancing data-driven customer analytics through academic-industry collaboration.
  • C. Perception, Opportunity and Profit
    Perception, Opportunity and Profit is a seminal work in Austrian economics by Israel Kirzner that analyzes the role of entrepreneurial discovery in market processes and profit generation.
  • D. Experience and Prediction
    Experience and Prediction is a seminal philosophical work by Hans Reichenbach that develops a logical and probabilistic foundation for scientific knowledge and induction within the framework of logical empiricism.
  • E. The Decline and Rise of the Consumer
    "The Decline and Rise of the Consumer" is a work by philosopher and cultural pluralism advocate Horace M. Kallen that examines the role, power, and rights of consumers within modern industrial and democratic society.
  • 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_69b345415ba481908df738e7174448ba completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b355d1eba08190899d0a3c1684ce4e completed March 13, 2026, 12:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69b613850eb88190b689a632b0e2b374 completed March 15, 2026, 2:03 a.m.
NEDg Description generation batch_69b61464b0dc81909cab007115435b8b completed March 15, 2026, 2:07 a.m.
NED2 Entity disambiguation (via description) batch_69b6151440648190bf8c1c95e20caf13 completed March 15, 2026, 2:10 a.m.
Created at: March 12, 2026, 11:32 p.m.