Triple

T29036689
Position Surface form Disambiguated ID Type / Status
Subject White Nile State E737879 entity
Predicate hasTransportCorridor P3034 FINISHED
Object Khartoum–Kosti route
The Khartoum–Kosti route is a major transport corridor in Sudan linking the capital Khartoum with the river port city of Kosti along the White Nile.
E1848725 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: Khartoum–Kosti route | Statement: [White Nile State, hasTransportCorridor, Khartoum–Kosti route]
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: Khartoum–Kosti route
Triple: [White Nile State, hasTransportCorridor, Khartoum–Kosti route]
Generated description
The Khartoum–Kosti route is a major transport corridor in Sudan linking the capital Khartoum with the river port city of Kosti along the White Nile.

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_69f077efb3848190b41574e1670f6ae2 completed April 28, 2026, 9:03 a.m.
NER Named-entity recognition batch_69f6603db2988190af8ce0beaaec257d completed May 2, 2026, 8:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a251f6b59008190bcb1b4ce741f8c49 completed June 7, 2026, 7:36 a.m.
NEDg Description generation batch_6a2525c1a39c81909377a6518c8e8f35 completed June 7, 2026, 8:03 a.m.
NED2 Entity disambiguation (via description) batch_6a252993dd108190994b02f7ee9d7762 completed June 7, 2026, 8:19 a.m.
Created at: April 28, 2026, 9:59 a.m.