Triple

T30187572
Position Surface form Disambiguated ID Type / Status
Subject NHS Highland E767378 entity
Predicate manages P86 FINISHED
Object Lorn and Islands Hospital
Lorn and Islands Hospital is a rural general hospital in Oban, Scotland, providing healthcare services to the town and surrounding island communities.
 E1903213 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: Lorn and Islands Hospital | Statement: [NHS Highland, manages, Lorn and Islands 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: Lorn and Islands Hospital
Triple: [NHS Highland, manages, Lorn and Islands Hospital]
Generated description
Lorn and Islands Hospital is a rural general hospital in Oban, Scotland, providing healthcare services to the town and surrounding island communities.

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_69f2247cc3d88190811dec3face94bf5 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67f8103cc819089e3ae3781984fdc completed May 2, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27586fd3a48190a0becdeba7740f09 completed June 9, 2026, 12:04 a.m.
NEDg Description generation batch_6a275a43c69c8190b8b17520ed80530f completed June 9, 2026, 12:11 a.m.
NED2 Entity disambiguation (via description) batch_6a275b94cf908190a828d9b24d444b01 completed June 9, 2026, 12:17 a.m.
Created at: April 29, 2026, 7:27 p.m.