Triple

T27039224
Position Surface form Disambiguated ID Type / Status
Subject Northern Province, Zambia E684440 entity
Predicate containsProtectedArea P855 FINISHED
Object Nsumbu National Park
Nsumbu National Park is a remote wildlife reserve in northern Zambia known for its diverse game, unspoiled wilderness, and scenic location along the shores of Lake Tanganyika.
 E1769258 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: Nsumbu National Park | Statement: [Northern Province, Zambia, containsProtectedArea, Nsumbu National Park]
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: Nsumbu National Park
Triple: [Northern Province, Zambia, containsProtectedArea, Nsumbu National Park]
Generated description
Nsumbu National Park is a remote wildlife reserve in northern Zambia known for its diverse game, unspoiled wilderness, and scenic location along the shores of Lake Tanganyika.

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_69ef148193c48190bb1a0cfae6a407c4 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f6226af8408190a9673ca09a5d4f87 completed May 2, 2026, 4:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7b58e2c8190a0dac5881e488c15 completed May 24, 2026, 7:24 a.m.
NEDg Description generation batch_6a12a8f06dd4819082b919c0eaf0195d completed May 24, 2026, 7:29 a.m.
NED2 Entity disambiguation (via description) batch_6a12a9da3fa0819084049ed2e7bfbd79 completed May 24, 2026, 7:33 a.m.
Created at: April 27, 2026, 8:03 a.m.