Triple

T350306
Position Surface form Disambiguated ID Type / Status
Subject College of Engineering, University of California, Berkeley E7426 entity
Predicate hasAcademicUnit P1488 FINISHED
Object Department of Materials Science and Engineering, UC Berkeley
The Department of Materials Science and Engineering at UC Berkeley is a leading academic department focused on the study, design, and engineering of materials for advanced technologies and scientific innovation.
E44889 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 Materials Science and Engineering, UC Berkeley | Statement: [College of Engineering, University of California, Berkeley, hasAcademicUnit, Department of Materials Science and Engineering, UC Berkeley]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Department of Materials Science and Engineering, UC Berkeley
Context triple: [College of Engineering, University of California, Berkeley, hasAcademicUnit, Department of Materials Science and Engineering, UC Berkeley]
  • A. Department of Materials Science and Engineering (Carnegie Mellon University)
    The Department of Materials Science and Engineering at Carnegie Mellon University is an academic unit specializing in the study, research, and education of materials and their applications in engineering and technology.
  • B. Department of Materials Science and Metallurgy, University of Cambridge
    The Department of Materials Science and Metallurgy at the University of Cambridge is a leading academic and research department specializing in the study, development, and engineering of materials and their properties.
  • C. Institute for Solid State Physics, University of Tokyo
    The Institute for Solid State Physics at the University of Tokyo is a leading Japanese research institute specializing in condensed matter physics and related materials science.
  • D. MIT Department of Civil and Environmental Engineering
    The MIT Department of Civil and Environmental Engineering is an academic department at the Massachusetts Institute of Technology focused on advancing infrastructure, environmental sustainability, and resilient systems through research and education in civil and environmental engineering.
  • E. Lawrence Berkeley National Laboratory
    Lawrence Berkeley National Laboratory is a U.S. Department of Energy national research laboratory in Berkeley, California, renowned for pioneering work in nuclear and particle physics, chemistry, and energy sciences.
  • 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 Materials Science and Engineering, UC Berkeley
Triple: [College of Engineering, University of California, Berkeley, hasAcademicUnit, Department of Materials Science and Engineering, UC Berkeley]
Generated description
The Department of Materials Science and Engineering at UC Berkeley is a leading academic department focused on the study, design, and engineering of materials for advanced technologies and scientific innovation.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Department of Materials Science and Engineering, UC Berkeley
Target entity description: The Department of Materials Science and Engineering at UC Berkeley is a leading academic department focused on the study, design, and engineering of materials for advanced technologies and scientific innovation.
  • A. Department of Materials Science and Engineering (Carnegie Mellon University)
    The Department of Materials Science and Engineering at Carnegie Mellon University is an academic unit specializing in the study, research, and education of materials and their applications in engineering and technology.
  • B. Department of Materials Science and Metallurgy, University of Cambridge
    The Department of Materials Science and Metallurgy at the University of Cambridge is a leading academic and research department specializing in the study, development, and engineering of materials and their properties.
  • C. Institute for Solid State Physics, University of Tokyo
    The Institute for Solid State Physics at the University of Tokyo is a leading Japanese research institute specializing in condensed matter physics and related materials science.
  • D. MIT Department of Civil and Environmental Engineering
    The MIT Department of Civil and Environmental Engineering is an academic department at the Massachusetts Institute of Technology focused on advancing infrastructure, environmental sustainability, and resilient systems through research and education in civil and environmental engineering.
  • E. Lawrence Berkeley National Laboratory
    Lawrence Berkeley National Laboratory is a U.S. Department of Energy national research laboratory in Berkeley, California, renowned for pioneering work in nuclear and particle physics, chemistry, and energy sciences.
  • 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_69a2e7e696948190bebc966535995e45 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2eb1f028c819098fa6480b4ca5cf0 completed Feb. 28, 2026, 1:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3dd33a50c8190846d19c24eb74039 completed March 1, 2026, 6:31 a.m.
NEDg Description generation batch_69a3ddca8d3c8190b5baafe999a26b58 completed March 1, 2026, 6:33 a.m.
NED2 Entity disambiguation (via description) batch_69a3de7f9b28819099cfe7c55a8168f0 completed March 1, 2026, 6:36 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.