Triple

T27821920
Position Surface form Disambiguated ID Type / Status
Subject Donington Park E702841 entity
Predicate formerEvent P130356 FINISHED
Object British Motorcycle Grand Prix
The British Motorcycle Grand Prix is a premier MotoGP World Championship race held annually in the United Kingdom, attracting top riders and manufacturers from around the world.
E1791961 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: British Motorcycle Grand Prix | Statement: [Donington Park, formerEvent, British Motorcycle Grand Prix]
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: British Motorcycle Grand Prix
Triple: [Donington Park, formerEvent, British Motorcycle Grand Prix]
Generated description
The British Motorcycle Grand Prix is a premier MotoGP World Championship race held annually in the United Kingdom, attracting top riders and manufacturers from around the world.

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_69ef840ad1e88190b5bff2d1ddec8700 completed April 27, 2026, 3:43 p.m.
NER Named-entity recognition batch_69f6386f0b5c8190bd8fbef0b1b06793 completed May 2, 2026, 5:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12f72842f08190a1c2e811c68ec2ca completed May 24, 2026, 1:03 p.m.
NEDg Description generation batch_6a12fb496c188190abbbcd5200aa5457 completed May 24, 2026, 1:21 p.m.
NED2 Entity disambiguation (via description) batch_6a12fbc87d94819097dbb89898b6ba03 completed May 24, 2026, 1:23 p.m.
Created at: April 27, 2026, 5:49 p.m.