Triple
T5799611
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gender Unit |
E128587
|
entity |
| Predicate | collaboratesWith |
P37
|
FINISHED |
| Object |
Integrated Operational Teams of UN DPO
The Integrated Operational Teams of the UN Department of Peace Operations are multidisciplinary teams that provide integrated political, operational, and strategic support to United Nations peacekeeping missions and special political missions in the field.
|
E548503
|
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: Integrated Operational Teams of UN DPO | Statement: [Gender Unit, collaboratesWith, Integrated Operational Teams of UN DPO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Integrated Operational Teams of UN DPO Context triple: [Gender Unit, collaboratesWith, Integrated Operational Teams of UN DPO]
-
A.
Peacekeeping Data and Analytics Team
The Peacekeeping Data and Analytics Team is a specialized unit that leverages data analysis and evidence-based insights to support planning, decision-making, and performance improvement in United Nations peacekeeping operations.
-
B.
Peacekeeping Knowledge Management and Lessons Learned Unit
The Peacekeeping Knowledge Management and Lessons Learned Unit is a specialized office within the UN peacekeeping architecture that captures, analyzes, and disseminates operational experience to improve the effectiveness of current and future peace operations.
-
C.
Brahimi Report on United Nations Peace Operations
The Brahimi Report on United Nations Peace Operations is a landmark 2000 UN study that critically assessed past peacekeeping failures and proposed major reforms to strengthen the effectiveness, credibility, and resources of UN peace operations.
-
D.
Act on Cooperation with United Nations Peacekeeping Operations
The Act on Cooperation with United Nations Peacekeeping Operations is a Japanese law that authorizes and regulates Japan’s participation in UN peacekeeping missions and related international peace cooperation activities.
-
E.
United Nations Humanitarian Assistance Coordination in Rwanda
The United Nations Humanitarian Assistance Coordination in Rwanda was a UN body established to organize, coordinate, and support international humanitarian relief efforts in Rwanda during and after the 1994 genocide and civil conflict.
- 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: Integrated Operational Teams of UN DPO Triple: [Gender Unit, collaboratesWith, Integrated Operational Teams of UN DPO]
Generated description
The Integrated Operational Teams of the UN Department of Peace Operations are multidisciplinary teams that provide integrated political, operational, and strategic support to United Nations peacekeeping missions and special political missions in the field.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Integrated Operational Teams of UN DPO Target entity description: The Integrated Operational Teams of the UN Department of Peace Operations are multidisciplinary teams that provide integrated political, operational, and strategic support to United Nations peacekeeping missions and special political missions in the field.
-
A.
Peacekeeping Data and Analytics Team
The Peacekeeping Data and Analytics Team is a specialized unit that leverages data analysis and evidence-based insights to support planning, decision-making, and performance improvement in United Nations peacekeeping operations.
-
B.
Peacekeeping Knowledge Management and Lessons Learned Unit
The Peacekeeping Knowledge Management and Lessons Learned Unit is a specialized office within the UN peacekeeping architecture that captures, analyzes, and disseminates operational experience to improve the effectiveness of current and future peace operations.
-
C.
Brahimi Report on United Nations Peace Operations
The Brahimi Report on United Nations Peace Operations is a landmark 2000 UN study that critically assessed past peacekeeping failures and proposed major reforms to strengthen the effectiveness, credibility, and resources of UN peace operations.
-
D.
Act on Cooperation with United Nations Peacekeeping Operations
The Act on Cooperation with United Nations Peacekeeping Operations is a Japanese law that authorizes and regulates Japan’s participation in UN peacekeeping missions and related international peace cooperation activities.
-
E.
United Nations Humanitarian Assistance Coordination in Rwanda
The United Nations Humanitarian Assistance Coordination in Rwanda was a UN body established to organize, coordinate, and support international humanitarian relief efforts in Rwanda during and after the 1994 genocide and civil conflict.
- 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_69c00846a0d881909e46841f8e156b64 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02acc621c8190958aaaa7d32d0c8b |
completed | March 22, 2026, 5:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c09833017c81908da09127e8455cb6 |
completed | March 23, 2026, 1:32 a.m. |
| NEDg | Description generation | batch_69c09a38077081909873a9f43f578d36 |
completed | March 23, 2026, 1:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c09aad318c81909420fa544676ce72 |
completed | March 23, 2026, 1:43 a.m. |
Created at: March 22, 2026, 3:52 p.m.