Triple
T10709151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fisher College of Business |
E252486
|
entity |
| Predicate | hasUnit |
P35
|
FINISHED |
| Object |
Department of Operations and Business Analytics
The Department of Operations and Business Analytics is an academic unit specializing in data-driven decision-making, process optimization, and analytical methods within business education and research.
|
E880513
|
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: Department of Operations and Business Analytics | Statement: [Fisher College of Business, hasUnit, Department of Operations and Business Analytics]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Department of Operations and Business Analytics Context triple: [Fisher College of Business, hasUnit, Department of Operations and Business Analytics]
-
A.
Department of Data Sciences and Operations
The Department of Data Sciences and Operations is an academic unit at the USC Marshall School of Business that focuses on research and education in data analytics, statistics, information systems, and operations management.
-
B.
Department of Business Analytics and Statistics
The Department of Business Analytics and Statistics is an academic unit specializing in data-driven decision-making, statistical analysis, and analytics education and research within the Haslam College of Business.
-
C.
Department of Operations and Decision Sciences
The Department of Operations and Decision Sciences is an academic unit specializing in operations management, business analytics, and quantitative decision-making within the Lazaridis School of Business and Economics.
-
D.
Department of Operations Research and Statistics
The Department of Operations Research and Statistics is an academic unit specializing in quantitative decision-making, optimization, and statistical analysis within the Faculty of Organizational Sciences at the University of Belgrade.
-
E.
Department of Operations and Information Management
The Department of Operations and Information Management is an academic unit specializing in operations management, business analytics, and information systems within the Isenberg School of Management.
- 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: Department of Operations and Business Analytics Triple: [Fisher College of Business, hasUnit, Department of Operations and Business Analytics]
Generated description
The Department of Operations and Business Analytics is an academic unit specializing in data-driven decision-making, process optimization, and analytical methods within business education and research.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Department of Operations and Business Analytics Target entity description: The Department of Operations and Business Analytics is an academic unit specializing in data-driven decision-making, process optimization, and analytical methods within business education and research.
-
A.
Department of Data Sciences and Operations
The Department of Data Sciences and Operations is an academic unit at the USC Marshall School of Business that focuses on research and education in data analytics, statistics, information systems, and operations management.
-
B.
Department of Business Analytics and Statistics
The Department of Business Analytics and Statistics is an academic unit specializing in data-driven decision-making, statistical analysis, and analytics education and research within the Haslam College of Business.
-
C.
Department of Operations and Decision Sciences
The Department of Operations and Decision Sciences is an academic unit specializing in operations management, business analytics, and quantitative decision-making within the Lazaridis School of Business and Economics.
-
D.
Department of Operations Research and Statistics
The Department of Operations Research and Statistics is an academic unit specializing in quantitative decision-making, optimization, and statistical analysis within the Faculty of Organizational Sciences at the University of Belgrade.
-
E.
Department of Operations and Information Management
The Department of Operations and Information Management is an academic unit specializing in operations management, business analytics, and information systems within the Isenberg School of Management.
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fe5063bc8190ba12fd68a59c9a03 |
completed | April 9, 2026, 1:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9990f220081909dae41bcec8b4768 |
completed | April 11, 2026, 12:42 a.m. |
| NEDg | Description generation | batch_69d99e8632688190b3746649a124ca09 |
completed | April 11, 2026, 1:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69da625a1e8c8190b282e7a70bb7c876 |
completed | April 11, 2026, 3:01 p.m. |
Created at: April 8, 2026, 9:13 p.m.