Triple

T28381776
Position Surface form Disambiguated ID Type / Status
Subject Dr George Mukhari Academic Hospital E718907 entity
Predicate namedAfter P63 FINISHED
Object George Mukhari
George Mukhari was a prominent South African medical professional and community figure after whom the Dr George Mukhari Academic Hospital is named.
 E1818772 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: George Mukhari | Statement: [Dr George Mukhari Academic Hospital, namedAfter, George Mukhari]
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: George Mukhari
Triple: [Dr George Mukhari Academic Hospital, namedAfter, George Mukhari]
Generated description
George Mukhari was a prominent South African medical professional and community figure after whom the Dr George Mukhari Academic Hospital is named.

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_69eff6ee5afc8190bd7375a29f0cc6c6 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64cb6c8708190b0a947d7da770920 completed May 2, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a164172b660819097876695b71bafea completed May 27, 2026, 12:57 a.m.
NEDg Description generation batch_6a16426a75c48190b5637f503a143bea completed May 27, 2026, 1:01 a.m.
NED2 Entity disambiguation (via description) batch_6a1643096a5c8190bd430174a11ce511 completed May 27, 2026, 1:04 a.m.
Created at: April 28, 2026, 1:07 a.m.