Triple

T7574249
Position Surface form Disambiguated ID Type / Status
Subject Dimensions E179322 entity
Predicate operatedBy P86 FINISHED
Object Digital Science E673853 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: Digital Science | Statement: [Dimensions, operatedBy, Digital Science]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Digital Science
Context triple: [Dimensions, operatedBy, Digital Science]
  • A. Digital Science chosen
    Digital Science is a technology company that provides research analytics, data, and software tools to support and improve the scholarly research ecosystem.
  • B. Digital Research
    Digital Research was a pioneering software company best known for creating early microcomputer operating systems such as CP/M and its 16-bit variants.
  • C. Scientific Data Systems
    Scientific Data Systems was an early computer company known for producing advanced scientific and real-time computing systems in the 1960s before being acquired by Xerox.
  • D. Web Science
    Web Science is an interdisciplinary field that studies the social, technical, and conceptual structures of the World Wide Web and its impact on society.
  • E. Digital Scholarship Lab
    The Digital Scholarship Lab is a Brown University Library facility that supports research and teaching through advanced digital tools, data visualization, and collaborative scholarly projects.
  • 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_69c69f316e50819081a271c85c06f918 completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c6f948e1e08190ad807292365a0c27 completed March 27, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69c861673ae48190b86e8023fd02771c completed March 28, 2026, 11:16 p.m.
Created at: March 27, 2026, 3:51 p.m.