Triple

T7520717
Position Surface form Disambiguated ID Type / Status
Subject USC Marshall School of Business E177760 entity
Predicate hasDepartment P35 FINISHED
Object 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.
E670137 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 Data Sciences and Operations | Statement: [USC Marshall School of Business, hasDepartment, Department of Data Sciences and Operations]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Department of Data Sciences and Operations
Context triple: [USC Marshall School of Business, hasDepartment, Department of Data Sciences and Operations]
  • A. 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.
  • 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 Information Systems and Analytics
    The Department of Information Systems and Analytics is an academic unit specializing in data-driven decision-making, information technology, and business analytics within the Leavey School of Business.
  • D. Office of Data Science
    The Office of Data Science is a specialized unit that applies advanced data analytics and quantitative methods to support the U.S. Securities and Exchange Commission’s economic, risk, and policy analysis.
  • E. Information Systems and Decision Sciences Department
    The Information Systems and Decision Sciences Department is an academic unit specializing in information technology, analytics, and data-driven decision-making within the Muma College of Business.
  • 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 Data Sciences and Operations
Triple: [USC Marshall School of Business, hasDepartment, Department of Data Sciences and Operations]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Department of Data Sciences and Operations
Target entity description: 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.
  • A. 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.
  • 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 Information Systems and Analytics
    The Department of Information Systems and Analytics is an academic unit specializing in data-driven decision-making, information technology, and business analytics within the Leavey School of Business.
  • D. Office of Data Science
    The Office of Data Science is a specialized unit that applies advanced data analytics and quantitative methods to support the U.S. Securities and Exchange Commission’s economic, risk, and policy analysis.
  • E. Information Systems and Decision Sciences Department
    The Information Systems and Decision Sciences Department is an academic unit specializing in information technology, analytics, and data-driven decision-making within the Muma College of Business.
  • 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_69c69f2891148190a484f3b8222c6f1b completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f7c2ad6c8190b822c0a5b80e7829 completed March 27, 2026, 9:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c84629f00c8190a64d51586bd3b96c completed March 28, 2026, 9:20 p.m.
NEDg Description generation batch_69c8471da6c481909b48db7ad6e9426d completed March 28, 2026, 9:24 p.m.
NED2 Entity disambiguation (via description) batch_69c848049f548190b8c9a9c3d3aeed45 completed March 28, 2026, 9:28 p.m.
Created at: March 27, 2026, 3:46 p.m.