Triple
T4099065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculty of Engineering, University of Sheffield |
E87893
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Department of Materials Science and Engineering, University of Sheffield
The Department of Materials Science and Engineering at the University of Sheffield is an academic department specializing in the research and teaching of materials science, engineering, and related technologies.
|
E412749
|
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, University of Sheffield | Statement: [Faculty of Engineering, University of Sheffield, hasPart, Department of Materials Science and Engineering, University of Sheffield]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Department of Materials Science and Engineering, University of Sheffield Context triple: [Faculty of Engineering, University of Sheffield, hasPart, Department of Materials Science and Engineering, University of Sheffield]
-
A.
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.
-
B.
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.
-
C.
Department of Materials Science and Engineering (University of Michigan)
The Department of Materials Science and Engineering at the University of Michigan is an academic department focused on research and education in the properties, design, and applications of materials for engineering and technology.
-
D.
Department of Materials Science and Engineering
The Department of Materials Science and Engineering is an academic unit focused on studying and developing materials and their properties for engineering and technological applications.
-
E.
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.
- 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, University of Sheffield Triple: [Faculty of Engineering, University of Sheffield, hasPart, Department of Materials Science and Engineering, University of Sheffield]
Generated description
The Department of Materials Science and Engineering at the University of Sheffield is an academic department specializing in the research and teaching of materials science, engineering, and related technologies.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Department of Materials Science and Engineering, University of Sheffield Target entity description: The Department of Materials Science and Engineering at the University of Sheffield is an academic department specializing in the research and teaching of materials science, engineering, and related technologies.
-
A.
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.
-
B.
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.
-
C.
Department of Materials Science and Engineering (University of Michigan)
The Department of Materials Science and Engineering at the University of Michigan is an academic department focused on research and education in the properties, design, and applications of materials for engineering and technology.
-
D.
Department of Materials Science and Engineering
The Department of Materials Science and Engineering is an academic unit focused on studying and developing materials and their properties for engineering and technological applications.
-
E.
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.
- 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_69aed94564cc8190a9c1457daedb6e7f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefd0bdea48190805a79515ee92709 |
completed | March 9, 2026, 5:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b56b7585bc81909dc2c02e60a55def |
completed | March 14, 2026, 2:06 p.m. |
| NEDg | Description generation | batch_69b56c3a4b708190a55027fd3b2b76e0 |
completed | March 14, 2026, 2:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b56cc5c704819083dac59bf7b3cb83 |
completed | March 14, 2026, 2:12 p.m. |
Created at: March 9, 2026, 3:40 p.m.