Triple

T26691303
Position Surface form Disambiguated ID Type / Status
Subject Murombedzi E672889 entity
Predicate partOf P40 FINISHED
Object Zimbabwean small towns network
The Zimbabwean small towns network is a collaborative association of smaller urban centers in Zimbabwe focused on shared development, governance, and service delivery initiatives.
 E1737578 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: Zimbabwean small towns network | Statement: [Murombedzi, partOf, Zimbabwean small towns network]
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: Zimbabwean small towns network
Triple: [Murombedzi, partOf, Zimbabwean small towns network]
Generated description
The Zimbabwean small towns network is a collaborative association of smaller urban centers in Zimbabwe focused on shared development, governance, and service delivery initiatives.

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_69eecda2066c8190a344218afa5e89c1 completed April 27, 2026, 2:44 a.m.
NER Named-entity recognition batch_69f617424a9881909cdec90173560455 completed May 2, 2026, 3:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11fe7c50608190a76095c47c931004 completed May 23, 2026, 7:22 p.m.
NEDg Description generation batch_6a11ff2e71988190ad6d34bc5420c9bd completed May 23, 2026, 7:25 p.m.
NED2 Entity disambiguation (via description) batch_6a11ffe6aad4819096be2e81c2f3d1b0 completed May 23, 2026, 7:28 p.m.
Created at: April 27, 2026, 3:26 a.m.