Triple

T24481164
Position Surface form Disambiguated ID Type / Status
Subject R532 road E617374 entity
Predicate maintainedBy P86 FINISHED
Object Mpumalanga provincial road authorities
Mpumalanga provincial road authorities are the regional government bodies responsible for planning, constructing, and maintaining the provincial road network within South Africa’s Mpumalanga province.
E1638957 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: Mpumalanga provincial road authorities | Statement: [R532 road, maintainedBy, Mpumalanga provincial road authorities]
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: Mpumalanga provincial road authorities
Triple: [R532 road, maintainedBy, Mpumalanga provincial road authorities]
Generated description
Mpumalanga provincial road authorities are the regional government bodies responsible for planning, constructing, and maintaining the provincial road network within South Africa’s Mpumalanga province.

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_69e2d7f3ae788190b683394db15f220e completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f29ed5d4388190a8a6ce4079aa8a54 completed April 30, 2026, 12:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fee7876788190a934cef90090bdf7 completed May 22, 2026, 5:49 a.m.
NEDg Description generation batch_6a0ff03ffa2c81908fe3c321029c784f completed May 22, 2026, 5:57 a.m.
NED2 Entity disambiguation (via description) batch_6a0ff10a3f508190bd92090d91a86020 completed May 22, 2026, 6 a.m.
Created at: April 18, 2026, 2:21 a.m.