Triple

T31115847
Position Surface form Disambiguated ID Type / Status
Subject Parkview Health E793082 entity
Predicate hasComponent P35 FINISHED
Object Parkview Ortho Hospital
Parkview Ortho Hospital is a specialized orthopedic hospital within the Parkview Health system, focused on the diagnosis, treatment, and surgical care of musculoskeletal conditions.
 E1951898 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: Parkview Ortho Hospital | Statement: [Parkview Health, hasComponent, Parkview Ortho 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: Parkview Ortho Hospital
Triple: [Parkview Health, hasComponent, Parkview Ortho Hospital]
Generated description
Parkview Ortho Hospital is a specialized orthopedic hospital within the Parkview Health system, focused on the diagnosis, treatment, and surgical care of musculoskeletal conditions.

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_69f224d0a7688190af3fe3e6e26d01ed completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f696ea40988190a9b30615cb65bcb3 completed May 3, 2026, 12:29 a.m.
NED1 Entity disambiguation (via context triple) batch_6a29590192a0819089fe43208c740c35 completed June 10, 2026, 12:30 p.m.
NEDg Description generation batch_6a2959a8c0708190b8cf96f13be2bd37 completed June 10, 2026, 12:33 p.m.
NED2 Entity disambiguation (via description) batch_6a295d91f59c819096a9baa006e93830 completed June 10, 2026, 12:50 p.m.
Created at: April 29, 2026, 9:04 p.m.