Triple

T33205233

Position	Surface form	Disambiguated ID	Type / Status
Subject	probenecid	`E850001`	entity
Predicate	hasDrugInteractionWith	`P149427`	FINISHED
Object	aspirin Aspirin is a widely used nonsteroidal anti-inflammatory drug (NSAID) that provides pain relief, reduces fever and inflammation, and at low doses helps prevent blood clots.	`E2042573`	NE FINISHED

How this triple was built (3 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: aspirin | Statement: [probenecid, hasDrugInteractionWith, aspirin]

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: aspirin
Triple: [probenecid, hasDrugInteractionWith, aspirin]

Generated description

Aspirin is a widely used nonsteroidal anti-inflammatory drug (NSAID) that provides pain relief, reduces fever and inflammation, and at low doses helps prevent blood clots.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: hasDrugInteractionWith
Context triple: [probenecid, hasDrugInteractionWith, aspirin]

A. hasCommonDrugInteraction chosen
Indicates that two drugs share at least one known interaction that may affect their safety or effectiveness when used together.
B. usesDrug
Indicates that an entity consumes, administers, or otherwise makes use of a specified drug.
C. hasDrug
Indicates that an entity possesses, is treated with, or is associated with a particular drug.
D. associatedWithDrug
Indicates that an entity has a relevant relationship or connection to a specific drug, such as use, exposure, or involvement in its context.
E. relatedDrug
Indicates that one drug has a specified relationship or association with another drug, such as interaction, similarity, or therapeutic linkage.
F. None of above.

Provenance (6 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_69f3495efedc8190843a5728089544b9`	completed	April 30, 2026, 12:21 p.m.
NER	Named-entity recognition	`batch_69f6dd3cc0648190a275812d6711275a`	completed	May 3, 2026, 5:29 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a352fc40600819092779618431f0ec2`	completed	June 19, 2026, 12:02 p.m.
NEDg	Description generation	`batch_6a353033d4808190b4d8096162a14557`	completed	June 19, 2026, 12:04 p.m.
NED2	Entity disambiguation (via description)	`batch_6a353298f99c8190a2774b4aab087295`	completed	June 19, 2026, 12:14 p.m.
PD	Predicate disambiguation	`batch_69f6d82eaee081908f06a71546315aea`	completed	May 3, 2026, 5:07 a.m.

Created at: May 1, 2026, 1:30 a.m.