Triple

T15103467
Position Surface form Disambiguated ID Type / Status
Subject Raghu Ramakrishnan E360726 entity
Predicate notableWork P4 FINISHED
Object Data Mining: Concepts and Techniques E566635 NE FINISHED

How this triple was built (2 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: Data Mining: Concepts and Techniques | Statement: [Raghu Ramakrishnan, notableWork, Data Mining: Concepts and Techniques]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Data Mining: Concepts and Techniques
Context triple: [Raghu Ramakrishnan, notableWork, Data Mining: Concepts and Techniques]
  • A. Data Mining: Concepts and Techniques chosen
    Data Mining: Concepts and Techniques is a widely used academic textbook that systematically introduces the principles, algorithms, and practical methods of data mining and knowledge discovery from large datasets.
  • B. Data Mining: The Textbook
    Data Mining: The Textbook is a comprehensive academic book that systematically covers the principles, algorithms, and applications of data mining and knowledge discovery in databases.
  • C. Mining of Massive Datasets
    "Mining of Massive Datasets" is a widely used textbook that introduces practical and scalable data mining and machine learning techniques for analyzing large-scale datasets.
  • D. Top 10 algorithms in data mining
    "Top 10 algorithms in data mining" is a widely cited survey paper that summarizes and evaluates the most influential data mining algorithms across key tasks such as classification, clustering, and association analysis.
  • E. IEEE International Conference on Data Mining
    The IEEE International Conference on Data Mining is a leading annual research conference that focuses on advances in data mining, machine learning, and knowledge discovery in databases.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d85a0491ec8190830960be8fafb994 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00551521c8190b48d1a074bb4bdfc completed April 15, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69feae274f6881908931569efc09996e completed May 9, 2026, 3:46 a.m.
Created at: April 10, 2026, 3:05 a.m.