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.