Triple

T14447113
Position Surface form Disambiguated ID Type / Status
Subject Xindong Wu E358236 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: [Xindong Wu, 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: [Xindong Wu, 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. 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.
  • C. 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.
  • D. 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.
  • E. ACM Transactions on Knowledge Discovery from Data
    ACM Transactions on Knowledge Discovery from Data is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data mining, knowledge discovery, and related areas of data science and machine learning.
  • 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_69d82794dfa081909b9134ad2e32244b completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de9160126c8190a2862a1a3dde1aff completed April 14, 2026, 7:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd6d86dbc0819085fef8fa8b45b373 completed May 8, 2026, 4:58 a.m.
Created at: April 10, 2026, 1:19 a.m.