Triple

T32597648
Position Surface form Disambiguated ID Type / Status
Subject Mossi people E833265 entity
Predicate historicalKingdom P20193 FINISHED
Object Kingdom of Tenkodogo
The Kingdom of Tenkodogo was an early Mossi state in what is now Burkina Faso, regarded as one of the foundational kingdoms of Mossi political and cultural history.
E2017949 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: Kingdom of Tenkodogo | Statement: [Mossi people, historicalKingdom, Kingdom of Tenkodogo]
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: Kingdom of Tenkodogo
Triple: [Mossi people, historicalKingdom, Kingdom of Tenkodogo]
Generated description
The Kingdom of Tenkodogo was an early Mossi state in what is now Burkina Faso, regarded as one of the foundational kingdoms of Mossi political and cultural history.

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_69f3492ab63c8190aec24d5003b47c29 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c69775b48190a940a490e9514992 completed May 3, 2026, 3:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a349ea854c081909e31e035d12f97db completed June 19, 2026, 1:43 a.m.
NEDg Description generation batch_6a349f1cf3288190ad3a15ae7016c12a completed June 19, 2026, 1:45 a.m.
NED2 Entity disambiguation (via description) batch_6a349f54fad881908d4fb08bf4d88008 completed June 19, 2026, 1:45 a.m.
Created at: May 1, 2026, 1:05 a.m.