Triple

T31384117
Position Surface form Disambiguated ID Type / Status
Subject Rochester General Hospital E800549 entity
Predicate affiliation P10 FINISHED
Object Rochester Regional Health
Rochester Regional Health is a large integrated healthcare system in the Rochester, New York area that operates hospitals, clinics, and other medical facilities to provide comprehensive care to the community.
E1961315 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: Rochester Regional Health | Statement: [Rochester General Hospital, affiliation, Rochester Regional Health]
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: Rochester Regional Health
Triple: [Rochester General Hospital, affiliation, Rochester Regional Health]
Generated description
Rochester Regional Health is a large integrated healthcare system in the Rochester, New York area that operates hospitals, clinics, and other medical facilities to provide comprehensive care to the community.

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_69f224e9d7048190b0cc20f9071bd3e4 completed April 29, 2026, 3:34 p.m.
NER Named-entity recognition batch_69f6a027b28881909990bde96515cc86 completed May 3, 2026, 1:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2ad23f8e948190919e37c20c6e87f6 completed June 11, 2026, 3:20 p.m.
NEDg Description generation batch_6a2ad2f799b88190bc53b018735fd99f completed June 11, 2026, 3:23 p.m.
NED2 Entity disambiguation (via description) batch_6a2ae02477f08190a5b9edab2c703eb5 completed June 11, 2026, 4:19 p.m.
Created at: April 29, 2026, 9:19 p.m.