Triple

T31719914
Position Surface form Disambiguated ID Type / Status
Subject Kwame Osei-Prempeh E809545 entity
Predicate educatedAt P5 FINISHED
Object Akonfudi Middle School
Akonfudi Middle School is a Ghanaian middle-level educational institution known as part of the early schooling of politician and lawyer Kwame Osei-Prempeh.
E1974437 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: Akonfudi Middle School | Statement: [Kwame Osei-Prempeh, educatedAt, Akonfudi Middle School]
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: Akonfudi Middle School
Triple: [Kwame Osei-Prempeh, educatedAt, Akonfudi Middle School]
Generated description
Akonfudi Middle School is a Ghanaian middle-level educational institution known as part of the early schooling of politician and lawyer Kwame Osei-Prempeh.

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_69f348e009c8819095d77df52c645b9c completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6aaf6aa908190b2c0f3a76353f486 completed May 3, 2026, 1:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b84d84a8081908667cc847f3114ed completed June 12, 2026, 4:02 a.m.
NEDg Description generation batch_6a2b867456c08190abf45115aef578bc completed June 12, 2026, 4:09 a.m.
NED2 Entity disambiguation (via description) batch_6a2b871f23dc8190aaabc38ea7f7ee27 completed June 12, 2026, 4:12 a.m.
Created at: April 30, 2026, 11:18 p.m.