Triple

T38493113
Position Surface form Disambiguated ID Type / Status
Subject Palapye E918108 entity
Predicate hasFacility P105 FINISHED
Object Palapye Primary Hospital
Palapye Primary Hospital is a local healthcare facility providing primary medical services to the community of Palapye, Botswana.
 E2272978 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: Palapye Primary Hospital | Statement: [Palapye, hasFacility, Palapye Primary Hospital]
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: Palapye Primary Hospital
Triple: [Palapye, hasFacility, Palapye Primary Hospital]
Generated description
Palapye Primary Hospital is a local healthcare facility providing primary medical services to the community of Palapye, Botswana.

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_69f76e9894208190a129a553a60ca58c completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fcd243878081909bf47996f9428045 completed May 7, 2026, 5:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41d64f550c8190849cad9c4402b4e9 completed June 29, 2026, 2:19 a.m.
NEDg Description generation batch_6a41d7cbe4f881908d9f904deda7bb65 completed June 29, 2026, 2:26 a.m.
NED2 Entity disambiguation (via description) batch_6a41d8484f88819089d64001a831ab40 completed June 29, 2026, 2:28 a.m.
Created at: May 3, 2026, 4:31 p.m.