Triple

T29750070
Position Surface form Disambiguated ID Type / Status
Subject Ministry of Defense of Djibouti E752868 entity
Predicate oversees P46 FINISHED
Object Republican Guard of Djibouti
The Republican Guard of Djibouti is an elite military unit responsible for protecting the president, key government institutions, and other strategic interests of the Djiboutian state.
E1894409 NE FINISHED

How this triple was built (2 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: Republican Guard of Djibouti | Statement: [Ministry of Defense of Djibouti, oversees, Republican Guard of Djibouti]
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: Republican Guard of Djibouti
Triple: [Ministry of Defense of Djibouti, oversees, Republican Guard of Djibouti]
Generated description
The Republican Guard of Djibouti is an elite military unit responsible for protecting the president, key government institutions, and other strategic interests of the Djiboutian state.

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_69f0d62c84cc8190846f80ae04fdf8ec completed April 28, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f6736a13f08190b9695bb8796e0aec completed May 2, 2026, 9:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2721d82eec81908dd6147437c58f42 completed June 8, 2026, 8:11 p.m.
NEDg Description generation batch_6a272333f384819084456b384bc17a6c completed June 8, 2026, 8:16 p.m.
NED2 Entity disambiguation (via description) batch_6a2723e6c6648190801fec9c9fd7f557 completed June 8, 2026, 8:19 p.m.
Created at: April 28, 2026, 7:53 p.m.