Triple

T26339677
Position Surface form Disambiguated ID Type / Status
Subject Jefferson Health E662609 entity
Predicate hasComponent P35 FINISHED
Object Jefferson Methodist Hospital
Jefferson Methodist Hospital is a community hospital in South Philadelphia that is part of the Jefferson Health system, providing a range of acute and specialty medical services.
E1720158 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: Jefferson Methodist Hospital | Statement: [Jefferson Health, hasComponent, Jefferson Methodist 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: Jefferson Methodist Hospital
Triple: [Jefferson Health, hasComponent, Jefferson Methodist Hospital]
Generated description
Jefferson Methodist Hospital is a community hospital in South Philadelphia that is part of the Jefferson Health system, providing a range of acute and specialty 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_69ee81304194819092e20e0fae3aee07 completed April 26, 2026, 9:18 p.m.
NER Named-entity recognition batch_69f60fa1a2e48190894526679b07d59d completed May 2, 2026, 2:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a119a5ddb8481909bc2c4b37fa01d85 completed May 23, 2026, 12:15 p.m.
NEDg Description generation batch_6a119ad502f4819094bacc5b50514200 completed May 23, 2026, 12:17 p.m.
NED2 Entity disambiguation (via description) batch_6a119b5af6f48190a607628edf5bd0c8 completed May 23, 2026, 12:19 p.m.
Created at: April 26, 2026, 10:38 p.m.