Triple
T13552348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculty of Health (Aarhus University) |
E323680
|
entity |
| Predicate | hasDepartment |
P35
|
FINISHED |
| Object |
Department of Forensic Medicine (Aarhus University)
The Department of Forensic Medicine at Aarhus University is an academic and research unit specializing in forensic pathology, clinical forensic medicine, and related medico-legal sciences in support of education, research, and the justice system.
|
E1047330
|
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 Forensic Medicine (Aarhus University) | Statement: [Faculty of Health (Aarhus University), hasDepartment, Department of Forensic Medicine (Aarhus University)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Department of Forensic Medicine (Aarhus University) Context triple: [Faculty of Health (Aarhus University), hasDepartment, Department of Forensic Medicine (Aarhus University)]
-
A.
School of Forensic Medicine
The School of Forensic Medicine is a specialized academic unit of Chongqing Medical University focused on education and research in forensic science and legal medicine.
-
B.
Department of Biomedical and Forensic Sciences
The Department of Biomedical and Forensic Sciences is an academic unit specializing in the study and research of biomedical science and forensic investigation within Anglia Ruskin University’s Faculty of Science and Engineering.
-
C.
Division of Forensic Sciences
The Division of Forensic Sciences is the forensic laboratory arm of the Georgia Bureau of Investigation, providing scientific analysis and expert testimony to support criminal investigations and prosecutions in the state of Georgia.
-
D.
Department of Forensic Sciences (District of Columbia)
The Department of Forensic Sciences (District of Columbia) is the city’s scientific agency responsible for providing forensic analysis and laboratory services to support criminal investigations and public safety efforts.
-
E.
Faculty of Health and Medical Sciences
The Faculty of Health and Medical Sciences is the University of Surrey’s academic division dedicated to education and research in healthcare, medicine, and related life 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 Forensic Medicine (Aarhus University) Triple: [Faculty of Health (Aarhus University), hasDepartment, Department of Forensic Medicine (Aarhus University)]
Generated description
The Department of Forensic Medicine at Aarhus University is an academic and research unit specializing in forensic pathology, clinical forensic medicine, and related medico-legal sciences in support of education, research, and the justice system.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Department of Forensic Medicine (Aarhus University) Target entity description: The Department of Forensic Medicine at Aarhus University is an academic and research unit specializing in forensic pathology, clinical forensic medicine, and related medico-legal sciences in support of education, research, and the justice system.
-
A.
School of Forensic Medicine
The School of Forensic Medicine is a specialized academic unit of Chongqing Medical University focused on education and research in forensic science and legal medicine.
-
B.
Department of Biomedical and Forensic Sciences
The Department of Biomedical and Forensic Sciences is an academic unit specializing in the study and research of biomedical science and forensic investigation within Anglia Ruskin University’s Faculty of Science and Engineering.
-
C.
Division of Forensic Sciences
The Division of Forensic Sciences is the forensic laboratory arm of the Georgia Bureau of Investigation, providing scientific analysis and expert testimony to support criminal investigations and prosecutions in the state of Georgia.
-
D.
Department of Forensic Sciences (District of Columbia)
The Department of Forensic Sciences (District of Columbia) is the city’s scientific agency responsible for providing forensic analysis and laboratory services to support criminal investigations and public safety efforts.
-
E.
Faculty of Health and Medical Sciences
The Faculty of Health and Medical Sciences is the University of Surrey’s academic division dedicated to education and research in healthcare, medicine, and related life 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_69d8076830b48190910a902bae5888e2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaff0a6548190b8cde5084cef0061 |
completed | April 12, 2026, 2:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75da721208190a3f5159125dbde9a |
completed | May 3, 2026, 2:37 p.m. |
| NEDg | Description generation | batch_69f75ec5101081909652b0c0998b36c8 |
completed | May 3, 2026, 2:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f75f4a3b0c81908c0ca0351771953b |
completed | May 3, 2026, 2:44 p.m. |
Created at: April 9, 2026, 9:46 p.m.