Triple

T36595258

Position	Surface form	Disambiguated ID	Type / Status
Subject	Nova Lisboa	`E902778`	entity
Predicate	partOf	`P40`	FINISHED
Object	Ovimbundu region The Ovimbundu region is a central highland area of Angola traditionally inhabited by the Ovimbundu people and historically centered around cities such as Nova Lisboa (now Huambo).	`E2195795`	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: Ovimbundu region | Statement: [Nova Lisboa, partOf, Ovimbundu region]

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: Ovimbundu region
Triple: [Nova Lisboa, partOf, Ovimbundu region]

Generated description

The Ovimbundu region is a central highland area of Angola traditionally inhabited by the Ovimbundu people and historically centered around cities such as Nova Lisboa (now Huambo).

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_69f76e6592e88190bac4eb00a46e9df9`	completed	May 3, 2026, 3:48 p.m.
NER	Named-entity recognition	`batch_69f7c30868988190bcc352b4ae184d48`	completed	May 3, 2026, 9:50 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a3a380efb1c81908ee75cfa0224c654`	completed	June 23, 2026, 7:38 a.m.
NEDg	Description generation	`batch_6a3a38e3e30481909d7058161151526d`	completed	June 23, 2026, 7:42 a.m.
NED2	Entity disambiguation (via description)	`batch_6a3a4125ebd88190b0e2ceb7030c44f5`	completed	June 23, 2026, 8:17 a.m.

Created at: May 3, 2026, 4:11 p.m.