Triple

T35388766
Position Surface form Disambiguated ID Type / Status
Subject Cloetesville E1022864 entity
Predicate partOfUrbanArea P294 FINISHED
Object Stellenbosch urban area
Stellenbosch urban area is the greater metropolitan region centered on the historic university town of Stellenbosch in South Africa’s Western Cape, encompassing its surrounding suburbs and townships.
E2139143 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: Stellenbosch urban area | Statement: [Cloetesville, partOfUrbanArea, Stellenbosch urban area]
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: Stellenbosch urban area
Triple: [Cloetesville, partOfUrbanArea, Stellenbosch urban area]
Generated description
Stellenbosch urban area is the greater metropolitan region centered on the historic university town of Stellenbosch in South Africa’s Western Cape, encompassing its surrounding suburbs and townships.

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_69f76df34ba48190bd80f0814cdcd540 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f794fa47048190b1605b16eb1bca4c completed May 3, 2026, 6:33 p.m.
NED1 Entity disambiguation (via context triple) batch_6a382cc3986c819080b99dd8cd90b6dc completed June 21, 2026, 6:26 p.m.
NEDg Description generation batch_6a382ddb48d081908b471af4a6fe70ff completed June 21, 2026, 6:30 p.m.
NED2 Entity disambiguation (via description) batch_6a382e98a29c8190baf0ade40125d39a completed June 21, 2026, 6:34 p.m.
Created at: May 3, 2026, 4:03 p.m.