Triple

T30099559
Position Surface form Disambiguated ID Type / Status
Subject Aikin family E764954 entity
Predicate hasMember P10 FINISHED
Object John Aikin (surgeon)
John Aikin was an 18th-century English surgeon and writer, notable as a central figure in the literary and intellectual Aikin family.
 E1899043 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: John Aikin (surgeon) | Statement: [Aikin family, hasMember, John Aikin (surgeon)]
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: John Aikin (surgeon)
Triple: [Aikin family, hasMember, John Aikin (surgeon)]
Generated description
John Aikin was an 18th-century English surgeon and writer, notable as a central figure in the literary and intellectual Aikin family.

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_69f22474e4288190b5f895fe3974aa92 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67d93f15c8190973da1ff08641d9c completed May 2, 2026, 10:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27432ee8308190be247b8290e59479 completed June 8, 2026, 10:33 p.m.
NEDg Description generation batch_6a274402537c8190ade00dfc5d92e722 completed June 8, 2026, 10:36 p.m.
NED2 Entity disambiguation (via description) batch_6a2744eb21688190939820a2659d99c5 completed June 8, 2026, 10:40 p.m.
Created at: April 29, 2026, 7:08 p.m.