Triple
T13183973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MIT Microsystems Technology Laboratories |
E313798
|
entity |
| Predicate | collaboratesWith |
P37
|
FINISHED |
| Object |
MIT Department of Materials Science and Engineering
The MIT Department of Materials Science and Engineering is a leading academic department at the Massachusetts Institute of Technology focused on researching and educating in the design, characterization, and engineering of materials for advanced technologies.
|
E1026970
|
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: MIT Department of Materials Science and Engineering | Statement: [MIT Microsystems Technology Laboratories, collaboratesWith, MIT Department of Materials Science and Engineering]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MIT Department of Materials Science and Engineering Context triple: [MIT Microsystems Technology Laboratories, collaboratesWith, MIT Department of Materials Science and Engineering]
-
A.
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.
-
B.
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.
-
C.
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.
-
D.
Materials Research Laboratory at MIT
The Materials Research Laboratory at MIT is a leading interdisciplinary research center focused on advancing the science and engineering of materials for applications ranging from electronics and energy to biotechnology.
-
E.
Institute of Materials Science and Engineering
The Institute of Materials Science and Engineering is a specialized research and academic unit at Hanoi University of Science and Technology focused on advanced materials, their properties, and engineering applications.
- 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: MIT Department of Materials Science and Engineering Triple: [MIT Microsystems Technology Laboratories, collaboratesWith, MIT Department of Materials Science and Engineering]
Generated description
The MIT Department of Materials Science and Engineering is a leading academic department at the Massachusetts Institute of Technology focused on researching and educating in the design, characterization, and engineering of materials for advanced technologies.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MIT Department of Materials Science and Engineering Target entity description: The MIT Department of Materials Science and Engineering is a leading academic department at the Massachusetts Institute of Technology focused on researching and educating in the design, characterization, and engineering of materials for advanced technologies.
-
A.
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.
-
B.
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.
-
C.
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.
-
D.
Materials Research Laboratory at MIT
The Materials Research Laboratory at MIT is a leading interdisciplinary research center focused on advancing the science and engineering of materials for applications ranging from electronics and energy to biotechnology.
-
E.
Institute of Materials Science and Engineering
The Institute of Materials Science and Engineering is a specialized research and academic unit at Hanoi University of Science and Technology focused on advanced materials, their properties, and engineering applications.
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c4a0b0081908027bf77442ff5ff |
completed | April 10, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6f5f307408190afd0df16a417c456 |
completed | May 3, 2026, 7:14 a.m. |
| NEDg | Description generation | batch_69f6f707d9e48190b772520ca9f4ac2c |
completed | May 3, 2026, 7:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6f85149cc8190adf68387475d3286 |
completed | May 3, 2026, 7:25 a.m. |
Created at: April 9, 2026, 9:15 p.m.