Triple

T2389286
Position Surface form Disambiguated ID Type / Status
Subject Ann Dowling E48901 entity
Predicate notableWork P4 FINISHED
Object Dowling Review of Business–University Research Collaborations
The Dowling Review of Business–University Research Collaborations is an influential report led by engineer Ann Dowling that examines how to strengthen partnerships between UK businesses and universities to drive innovation and economic growth.
E261944 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: Dowling Review of Business–University Research Collaborations | Statement: [Ann Dowling, notableWork, Dowling Review of Business–University Research Collaborations]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dowling Review of Business–University Research Collaborations
Context triple: [Ann Dowling, notableWork, Dowling Review of Business–University Research Collaborations]
  • A. Technical Corrections to the Bayh–Dole Act
    Technical Corrections to the Bayh–Dole Act is a legislative measure that refined and clarified the original Bayh–Dole Act’s provisions governing the ownership and commercialization of inventions arising from federally funded research.
  • B. Knowledge and Innovation Communities
    Knowledge and Innovation Communities are large-scale, EU-backed partnerships that bring together businesses, research institutions, and universities to drive innovation, entrepreneurship, and education in specific thematic areas.
  • C. Associated Countries to the European Research Area
    Associated Countries to the European Research Area are non-EU states that participate in the EU’s research and innovation framework programs under similar conditions to member countries, fostering integrated scientific collaboration across Europe.
  • D. Science and Engineering Indicators report
    The Science and Engineering Indicators report is a comprehensive, data-driven assessment of the state of U.S. and global science, technology, engineering, and mathematics (STEM) research, education, and innovation.
  • E. Centre for Business Research
    The Centre for Business Research is a research institute at the University of Cambridge that focuses on the study of enterprise, innovation, and economic performance, particularly in relation to policy and business practice.
  • 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: Dowling Review of Business–University Research Collaborations
Triple: [Ann Dowling, notableWork, Dowling Review of Business–University Research Collaborations]
Generated description
The Dowling Review of Business–University Research Collaborations is an influential report led by engineer Ann Dowling that examines how to strengthen partnerships between UK businesses and universities to drive innovation and economic growth.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Dowling Review of Business–University Research Collaborations
Target entity description: The Dowling Review of Business–University Research Collaborations is an influential report led by engineer Ann Dowling that examines how to strengthen partnerships between UK businesses and universities to drive innovation and economic growth.
  • A. Technical Corrections to the Bayh–Dole Act
    Technical Corrections to the Bayh–Dole Act is a legislative measure that refined and clarified the original Bayh–Dole Act’s provisions governing the ownership and commercialization of inventions arising from federally funded research.
  • B. Knowledge and Innovation Communities
    Knowledge and Innovation Communities are large-scale, EU-backed partnerships that bring together businesses, research institutions, and universities to drive innovation, entrepreneurship, and education in specific thematic areas.
  • C. Associated Countries to the European Research Area
    Associated Countries to the European Research Area are non-EU states that participate in the EU’s research and innovation framework programs under similar conditions to member countries, fostering integrated scientific collaboration across Europe.
  • D. Science and Engineering Indicators report
    The Science and Engineering Indicators report is a comprehensive, data-driven assessment of the state of U.S. and global science, technology, engineering, and mathematics (STEM) research, education, and innovation.
  • E. Centre for Business Research
    The Centre for Business Research is a research institute at the University of Cambridge that focuses on the study of enterprise, innovation, and economic performance, particularly in relation to policy and business practice.
  • 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_69a88aa5f63081908d07fd302029fcbd completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc7dca9248190b634ae9e02f6899a completed March 7, 2026, 6:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3d2ad588190a2cf3adab6a9404d completed March 9, 2026, 11:49 a.m.
NEDg Description generation batch_69aeb40b12d0819092b6c441580698a5 completed March 9, 2026, 11:50 a.m.
NED2 Entity disambiguation (via description) batch_69aeb471c0408190bfb2341899d44dba completed March 9, 2026, 11:52 a.m.
Created at: March 4, 2026, 7:57 p.m.