Triple

T25276015
Position Surface form Disambiguated ID Type / Status
Subject Ndola E633696 entity
Predicate hasAirport P105 FINISHED
Object Simon Mwansa Kapwepwe International Airport
Simon Mwansa Kapwepwe International Airport is a major Zambian international airport serving the city of Ndola and the Copperbelt region.
E1692140 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: Simon Mwansa Kapwepwe International Airport | Statement: [Ndola, hasAirport, Simon Mwansa Kapwepwe International Airport]
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: Simon Mwansa Kapwepwe International Airport
Triple: [Ndola, hasAirport, Simon Mwansa Kapwepwe International Airport]
Generated description
Simon Mwansa Kapwepwe International Airport is a major Zambian international airport serving the city of Ndola and the Copperbelt region.

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_69e75a92f48881909974ff9c11150a2e completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f48ba6eb208190823b251f5888044a completed May 1, 2026, 11:16 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10c117961081908a667d2053857a3d completed May 22, 2026, 8:48 p.m.
NEDg Description generation batch_6a10c4eb37cc8190b7c3440b77e8336e completed May 22, 2026, 9:04 p.m.
NED2 Entity disambiguation (via description) batch_6a10c5487a008190aa865554f445ab5e completed May 22, 2026, 9:06 p.m.
Created at: April 21, 2026, 1:17 p.m.