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.