Triple

T23474733
Position Surface form Disambiguated ID Type / Status
Subject Thaba Chweu Local Municipality E570226 entity
Predicate borderedBy P224 FINISHED
Object Steve Tshwete Local Municipality
Steve Tshwete Local Municipality is a local government area in Mpumalanga, South Africa, centered on the town of Middelburg and known for its coal mining and industrial activities.
E1745117 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: Steve Tshwete Local Municipality | Statement: [Thaba Chweu Local Municipality, borderedBy, Steve Tshwete Local Municipality]
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: Steve Tshwete Local Municipality
Triple: [Thaba Chweu Local Municipality, borderedBy, Steve Tshwete Local Municipality]
Generated description
Steve Tshwete Local Municipality is a local government area in Mpumalanga, South Africa, centered on the town of Middelburg and known for its coal mining and industrial activities.

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_69e245af8a88819084f2704f6d265a92 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1a704e2a48190acb55f77a2124412 completed April 29, 2026, 6:36 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1212fad444819092586d801cfa28da completed May 23, 2026, 8:50 p.m.
NEDg Description generation batch_6a12154f36408190ac8deb5e9359489f completed May 23, 2026, 8:59 p.m.
NED2 Entity disambiguation (via description) batch_6a121600e8f081909f5deb07266e1a80 completed May 23, 2026, 9:02 p.m.
Created at: April 17, 2026, 6 p.m.