Triple

T36237597
Position Surface form Disambiguated ID Type / Status
Subject Piedmont Healthcare E891415 entity
Predicate hasComponent P35 FINISHED
Object Piedmont Newton Hospital
Piedmont Newton Hospital is a community hospital in Georgia that is part of the Piedmont Healthcare system, providing a range of inpatient, outpatient, and emergency medical services.
E2182669 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 Newton Hospital | Statement: [Piedmont Healthcare, hasComponent, Piedmont Newton 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 Newton Hospital
Triple: [Piedmont Healthcare, hasComponent, Piedmont Newton Hospital]
Generated description
Piedmont Newton Hospital is a community hospital in Georgia that is part of the Piedmont Healthcare system, providing a range of inpatient, outpatient, and emergency 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_6a39b41babbc819095b993b7bbb4211a completed June 22, 2026, 10:15 p.m.
NEDg Description generation batch_6a39b7589c8c81909da9923396fefe6e completed June 22, 2026, 10:29 p.m.
NED2 Entity disambiguation (via description) batch_6a39bc45e43881908e3374a9e7d28cfb completed June 22, 2026, 10:50 p.m.
Created at: May 3, 2026, 4:09 p.m.