Triple

T25603397
Position Surface form Disambiguated ID Type / Status
Subject Patient Safety and Quality Improvement Act of 2005 E641844 entity
Predicate shortName P43 FINISHED
Object PSQIA
PSQIA is a U.S. federal law designed to improve patient safety by encouraging the confidential reporting and analysis of medical errors and adverse events.
 E1687172 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: PSQIA | Statement: [Patient Safety and Quality Improvement Act of 2005, shortName, PSQIA]
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: PSQIA
Triple: [Patient Safety and Quality Improvement Act of 2005, shortName, PSQIA]
Generated description
PSQIA is a U.S. federal law designed to improve patient safety by encouraging the confidential reporting and analysis of medical errors and adverse events.

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_69e75dc6ccf081908d49578fd36a76d5 completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f9a89830819083b11f0b818e538d completed May 2, 2026, 1:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10b772c92481909ec1a4202792eb35 completed May 22, 2026, 8:07 p.m.
NEDg Description generation batch_6a10b8265e8c8190817bca20ada4c82a completed May 22, 2026, 8:10 p.m.
NED2 Entity disambiguation (via description) batch_6a10b9651af481909206495b2fc57a2e completed May 22, 2026, 8:15 p.m.
Created at: April 21, 2026, 4:36 p.m.