Triple

T31766883
Position Surface form Disambiguated ID Type / Status
Subject Miria Obote E810833 entity
Predicate positionHeld P8 FINISHED
Object First Lady of Uganda
The First Lady of Uganda is the unofficial but influential title given to the wife of the serving President of Uganda, who often plays prominent roles in social, charitable, and political activities in the country.
E1976332 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: First Lady of Uganda | Statement: [Miria Obote, positionHeld, First Lady of Uganda]
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: First Lady of Uganda
Triple: [Miria Obote, positionHeld, First Lady of Uganda]
Generated description
The First Lady of Uganda is the unofficial but influential title given to the wife of the serving President of Uganda, who often plays prominent roles in social, charitable, and political activities in the country.

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_69f348e463e08190b902d4819195e1f0 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6ababec748190a987a80aa44477c3 completed May 3, 2026, 1:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b94928f748190b213748fc86149a3 completed June 12, 2026, 5:09 a.m.
NEDg Description generation batch_6a2b96bf9808819094d3d58bb4cf7d5a completed June 12, 2026, 5:18 a.m.
NED2 Entity disambiguation (via description) batch_6a2b997d478c8190b4c2fb8e24c47d03 completed June 12, 2026, 5:30 a.m.
Created at: April 30, 2026, 11:32 p.m.