Triple

T26857433
Position Surface form Disambiguated ID Type / Status
Subject OHSU Epilepsy Center E676232 entity
Predicate affiliatedWith P254 FINISHED
Object OHSU Hospital
OHSU Hospital is a major academic medical center in Portland, Oregon, serving as the primary teaching hospital for Oregon Health & Science University and providing advanced specialty and tertiary care.
 E329211 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: OHSU Hospital | Statement: [OHSU Epilepsy Center, affiliatedWith, OHSU 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: OHSU Hospital
Triple: [OHSU Epilepsy Center, affiliatedWith, OHSU Hospital]
Generated description
OHSU Hospital is a major academic medical center in Portland, Oregon, serving as the primary teaching hospital for Oregon Health & Science University and providing advanced specialty and tertiary care.

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_69eee9b9d7708190a15d7485709ae981 completed April 27, 2026, 4:44 a.m.
NER Named-entity recognition batch_69f61b98322881908adb98b258af26d5 completed May 2, 2026, 3:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1247e12c288190bfbf50c2e46bc042 completed May 24, 2026, 12:35 a.m.
NEDg Description generation batch_6a12488822208190aab1355ac3efd2a6 completed May 24, 2026, 12:38 a.m.
NED2 Entity disambiguation (via description) batch_6a124935c01c8190b9d6d13c4f50a104 completed May 24, 2026, 12:41 a.m.
Created at: April 27, 2026, 5:22 a.m.