Triple
T8541374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Upper Kabete Campus |
E202203
|
entity |
| Predicate | hostsDepartment |
P3556
|
FINISHED |
| Object |
Department of Land Resource Management and Agricultural Technology, University of Nairobi
The Department of Land Resource Management and Agricultural Technology at the University of Nairobi is an academic unit specializing in the study, management, and sustainable use of land and agricultural resources through teaching, research, and extension services.
|
E741148
|
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 Land Resource Management and Agricultural Technology, University of Nairobi | Statement: [Upper Kabete Campus, hostsDepartment, Department of Land Resource Management and Agricultural Technology, University of Nairobi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Department of Land Resource Management and Agricultural Technology, University of Nairobi Context triple: [Upper Kabete Campus, hostsDepartment, Department of Land Resource Management and Agricultural Technology, University of Nairobi]
-
A.
Academy of Bioresources and Environmental Management
The Academy of Bioresources and Environmental Management is a specialized faculty of Crimean Federal University focused on education and research in agriculture, natural resources, and environmental protection.
-
B.
Department of Land Economy, University of Cambridge
The Department of Land Economy at the University of Cambridge is an interdisciplinary academic department focusing on land, real estate, environmental policy, and spatial planning within an economics and law framework.
-
C.
Centre for Dryland Agriculture
The Centre for Dryland Agriculture is a research and training institute focused on improving agricultural productivity, sustainability, and livelihoods in dryland and semi-arid regions.
-
D.
Resource and Rural Economics Division
The Resource and Rural Economics Division is a unit of the U.S. Department of Agriculture’s Economic Research Service that conducts research and analysis on natural resources, environmental policy, and rural economic issues.
-
E.
Faculty of Agriculture and Natural Resources Management
The Faculty of Agriculture and Natural Resources Management is an academic division specializing in agricultural sciences, environmental stewardship, and sustainable natural resource management.
- 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 Land Resource Management and Agricultural Technology, University of Nairobi Triple: [Upper Kabete Campus, hostsDepartment, Department of Land Resource Management and Agricultural Technology, University of Nairobi]
Generated description
The Department of Land Resource Management and Agricultural Technology at the University of Nairobi is an academic unit specializing in the study, management, and sustainable use of land and agricultural resources through teaching, research, and extension services.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Department of Land Resource Management and Agricultural Technology, University of Nairobi Target entity description: The Department of Land Resource Management and Agricultural Technology at the University of Nairobi is an academic unit specializing in the study, management, and sustainable use of land and agricultural resources through teaching, research, and extension services.
-
A.
Academy of Bioresources and Environmental Management
The Academy of Bioresources and Environmental Management is a specialized faculty of Crimean Federal University focused on education and research in agriculture, natural resources, and environmental protection.
-
B.
Department of Land Economy, University of Cambridge
The Department of Land Economy at the University of Cambridge is an interdisciplinary academic department focusing on land, real estate, environmental policy, and spatial planning within an economics and law framework.
-
C.
Centre for Dryland Agriculture
The Centre for Dryland Agriculture is a research and training institute focused on improving agricultural productivity, sustainability, and livelihoods in dryland and semi-arid regions.
-
D.
Resource and Rural Economics Division
The Resource and Rural Economics Division is a unit of the U.S. Department of Agriculture’s Economic Research Service that conducts research and analysis on natural resources, environmental policy, and rural economic issues.
-
E.
Faculty of Agriculture and Natural Resources Management
The Faculty of Agriculture and Natural Resources Management is an academic division specializing in agricultural sciences, environmental stewardship, and sustainable natural resource management.
- 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_69ca832461e88190a654c5e44e233aa8 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc4578d9c8819096b3853d01c3ec11 |
completed | March 31, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce6da3d65c819087ed6b46dfc35885 |
completed | April 2, 2026, 1:22 p.m. |
| NEDg | Description generation | batch_69ce6ec3b080819082d64646d453541d |
completed | April 2, 2026, 1:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce6fe928d48190824e7a94fea5cfc0 |
completed | April 2, 2026, 1:32 p.m. |
Created at: March 30, 2026, 6:18 p.m.