Triple

T25285040
Position Surface form Disambiguated ID Type / Status
Subject African Rainbow Capital E633914 entity
Predicate keyPerson P256 FINISHED
Object Johan van der Merwe
Johan van der Merwe is a South African business executive and investor best known for his leadership role at investment holding company African Rainbow Capital.
E1692144 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: Johan van der Merwe | Statement: [African Rainbow Capital, keyPerson, Johan van der Merwe]
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: Johan van der Merwe
Triple: [African Rainbow Capital, keyPerson, Johan van der Merwe]
Generated description
Johan van der Merwe is a South African business executive and investor best known for his leadership role at investment holding company African Rainbow Capital.

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_69e75a9402fc81909362ca85277c06d9 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f48e0800a881909bf6099d120338a5 completed May 1, 2026, 11:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10c117961081908a667d2053857a3d completed May 22, 2026, 8:48 p.m.
NEDg Description generation batch_6a10c4eb37cc8190b7c3440b77e8336e completed May 22, 2026, 9:04 p.m.
NED2 Entity disambiguation (via description) batch_6a10c5487a008190aa865554f445ab5e completed May 22, 2026, 9:06 p.m.
Created at: April 21, 2026, 1:19 p.m.