Triple

T36237599
Position Surface form Disambiguated ID Type / Status
Subject Piedmont Healthcare E891415 entity
Predicate hasComponent P35 FINISHED
Object Piedmont Walton Hospital
Piedmont Walton Hospital is a community hospital in Georgia that is part of the Piedmont Healthcare system, providing a range of inpatient and outpatient medical services.
E2184795 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: Piedmont Walton Hospital | Statement: [Piedmont Healthcare, hasComponent, Piedmont Walton 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: Piedmont Walton Hospital
Triple: [Piedmont Healthcare, hasComponent, Piedmont Walton Hospital]
Generated description
Piedmont Walton Hospital is a community hospital in Georgia that is part of the Piedmont Healthcare system, providing a range of inpatient and outpatient medical services.

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_69f76e4387048190a1b27bcbf4ec7423 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b5ccbda481908fe1945c35e36ce8 completed May 3, 2026, 8:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39cfb3ebe481909a3384ec0fd5f120 completed June 23, 2026, 12:13 a.m.
NEDg Description generation batch_6a39d0835f7881909e2f9f19aa336e79 completed June 23, 2026, 12:17 a.m.
NED2 Entity disambiguation (via description) batch_6a39d149d0c88190b232b80550967869 completed June 23, 2026, 12:20 a.m.
Created at: May 3, 2026, 4:09 p.m.