Triple

T23287678
Position Surface form Disambiguated ID Type / Status
Subject Mbombela Local Municipality E589933 entity
Predicate hasMajorRiver P165 FINISHED
Object Nsikazi River
The Nsikazi River is a significant watercourse in South Africa’s Mpumalanga province that plays an important role in the ecology and communities of the Mbombela area.
E1689470 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: Nsikazi River | Statement: [Mbombela Local Municipality, hasMajorRiver, Nsikazi River]
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: Nsikazi River
Triple: [Mbombela Local Municipality, hasMajorRiver, Nsikazi River]
Generated description
The Nsikazi River is a significant watercourse in South Africa’s Mpumalanga province that plays an important role in the ecology and communities of the Mbombela area.

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_69e25d1af9d88190a0b9b5e8fa608618 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f19648842c81909756be4bc06b3a45 completed April 29, 2026, 5:25 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10c0fda8488190a17529d2846b5b1a completed May 22, 2026, 8:47 p.m.
NEDg Description generation batch_6a10c2eee95481908b782308c2a2e5cc completed May 22, 2026, 8:56 p.m.
NED2 Entity disambiguation (via description) batch_6a10c36b20a0819084d94066362937ee completed May 22, 2026, 8:58 p.m.
Created at: April 17, 2026, 5 p.m.