Triple
T360597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculty of Medicine and Health Sciences (McGill University) |
E7841
|
entity |
| Predicate | hasUnit |
P35
|
FINISHED |
| Object |
Department of Epidemiology, Biostatistics and Occupational Health
The Department of Epidemiology, Biostatistics and Occupational Health is an academic unit at McGill University specializing in research and graduate education on population health, statistical methods, and workplace health risks.
|
E45743
|
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 Epidemiology, Biostatistics and Occupational Health | Statement: [Faculty of Medicine and Health Sciences (McGill University), hasUnit, Department of Epidemiology, Biostatistics and Occupational Health]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Department of Epidemiology, Biostatistics and Occupational Health Context triple: [Faculty of Medicine and Health Sciences (McGill University), hasUnit, Department of Epidemiology, Biostatistics and Occupational Health]
-
A.
Department of Biostatistics
The Department of Biostatistics at the Harvard T.H. Chan School of Public Health is a leading academic center for developing and applying statistical methods to advance biomedical, public health, and quantitative science research.
-
B.
Department of Health Policy and Management
The Department of Health Policy and Management is an academic unit at the Harvard T.H. Chan School of Public Health focused on research and education in health policy, health systems, and healthcare management.
-
C.
Department of Global Health and Population
The Department of Global Health and Population is an academic department at the Harvard T.H. Chan School of Public Health focused on research, education, and policy to improve health and reduce inequalities worldwide.
-
D.
School of Public Health
The School of Public Health at the University of California, Berkeley is a leading institution dedicated to research, education, and practice in public health disciplines such as epidemiology, environmental health, health policy, and global health.
-
E.
Division of Cancer Control and Population Sciences
The Division of Cancer Control and Population Sciences is a branch of the National Cancer Institute that leads research and programs focused on understanding cancer burden, risk factors, prevention, and outcomes at the population level.
- 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 Epidemiology, Biostatistics and Occupational Health Triple: [Faculty of Medicine and Health Sciences (McGill University), hasUnit, Department of Epidemiology, Biostatistics and Occupational Health]
Generated description
The Department of Epidemiology, Biostatistics and Occupational Health is an academic unit at McGill University specializing in research and graduate education on population health, statistical methods, and workplace health risks.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Department of Epidemiology, Biostatistics and Occupational Health Target entity description: The Department of Epidemiology, Biostatistics and Occupational Health is an academic unit at McGill University specializing in research and graduate education on population health, statistical methods, and workplace health risks.
-
A.
Department of Biostatistics
The Department of Biostatistics at the Harvard T.H. Chan School of Public Health is a leading academic center for developing and applying statistical methods to advance biomedical, public health, and quantitative science research.
-
B.
Department of Health Policy and Management
The Department of Health Policy and Management is an academic unit at the Harvard T.H. Chan School of Public Health focused on research and education in health policy, health systems, and healthcare management.
-
C.
Department of Global Health and Population
The Department of Global Health and Population is an academic department at the Harvard T.H. Chan School of Public Health focused on research, education, and policy to improve health and reduce inequalities worldwide.
-
D.
School of Public Health
The School of Public Health at the University of California, Berkeley is a leading institution dedicated to research, education, and practice in public health disciplines such as epidemiology, environmental health, health policy, and global health.
-
E.
Division of Cancer Control and Population Sciences
The Division of Cancer Control and Population Sciences is a branch of the National Cancer Institute that leads research and programs focused on understanding cancer burden, risk factors, prevention, and outcomes at the population level.
- 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_69a2e7e880008190a6ad7e06e5d03007 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebccb8d88190a31f7c443a0c8566 |
completed | Feb. 28, 2026, 1:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3e57a56e081909004fcd7e15f457c |
completed | March 1, 2026, 7:06 a.m. |
| NEDg | Description generation | batch_69a3e5e50b848190ab5d036719048df8 |
completed | March 1, 2026, 7:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a3e63c749c8190b5bfd85b7ccafa9c |
completed | March 1, 2026, 7:09 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.