Triple
T12599534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Oxford Faculty of Mathematical, Physical and Life Sciences |
E300819
|
entity |
| Predicate | hasUnit |
P35
|
FINISHED |
| Object |
Department of Materials, University of Oxford
The Department of Materials at the University of Oxford is a leading academic and research department specializing in materials science and engineering, known for its work on the structure, properties and applications of advanced materials.
|
E991872
|
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, University of Oxford | Statement: [University of Oxford Faculty of Mathematical, Physical and Life Sciences, hasUnit, Department of Materials, University of Oxford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Department of Materials, University of Oxford Context triple: [University of Oxford Faculty of Mathematical, Physical and Life Sciences, hasUnit, Department of Materials, University of Oxford]
-
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, Imperial College London
The Department of Materials at Imperial College London is a leading academic and research department specializing in materials science and engineering, known for its cutting-edge work in areas such as nanotechnology, biomaterials, and structural materials.
-
C.
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.
-
D.
Department of Physics, University of Oxford
The Department of Physics at the University of Oxford is a leading global center for research and teaching in physics, encompassing a wide range of theoretical and experimental disciplines.
-
E.
UCL Centre for Materials Discovery
The UCL Centre for Materials Discovery is a research centre at University College London focused on developing and characterising advanced materials through interdisciplinary collaboration.
- 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, University of Oxford Triple: [University of Oxford Faculty of Mathematical, Physical and Life Sciences, hasUnit, Department of Materials, University of Oxford]
Generated description
The Department of Materials at the University of Oxford is a leading academic and research department specializing in materials science and engineering, known for its work on the structure, properties and applications of advanced materials.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Department of Materials, University of Oxford Target entity description: The Department of Materials at the University of Oxford is a leading academic and research department specializing in materials science and engineering, known for its work on the structure, properties and applications of advanced materials.
-
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, Imperial College London
The Department of Materials at Imperial College London is a leading academic and research department specializing in materials science and engineering, known for its cutting-edge work in areas such as nanotechnology, biomaterials, and structural materials.
-
C.
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.
-
D.
Department of Physics, University of Oxford
The Department of Physics at the University of Oxford is a leading global center for research and teaching in physics, encompassing a wide range of theoretical and experimental disciplines.
-
E.
UCL Centre for Materials Discovery
The UCL Centre for Materials Discovery is a research centre at University College London focused on developing and characterising advanced materials through interdisciplinary collaboration.
- 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_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954d096d08190afa1f685bad68d35 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ec92c6c8190bd2d193e70940407 |
completed | May 2, 2026, 8:30 p.m. |
| NEDg | Description generation | batch_69f65faf33e0819092df07a5fa98cb73 |
completed | May 2, 2026, 8:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f66036f520819098af75cd5578d573 |
completed | May 2, 2026, 8:36 p.m. |
Created at: April 9, 2026, 5:09 p.m.