Triple

T24796428
Position Surface form Disambiguated ID Type / Status
Subject Hoare family E620395 entity
Predicate hasMember P10 FINISHED
Object John Gurney Hoare
John Gurney Hoare was a 19th-century English banker and philanthropist from the prominent Quaker Hoare family.
 E1666013 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 Gurney Hoare | Statement: [Hoare family, hasMember, John Gurney Hoare]
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 Gurney Hoare
Triple: [Hoare family, hasMember, John Gurney Hoare]
Generated description
John Gurney Hoare was a 19th-century English banker and philanthropist from the prominent Quaker Hoare 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_69e2fabe77c8819085f7ce6486248139 completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f412a660648190a343347e6ff36ea5 completed May 1, 2026, 2:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a105cc9dbe08190bde3760242597006 completed May 22, 2026, 1:40 p.m.
NEDg Description generation batch_6a105e66f3b08190a2c6d28fc8a3cf4b completed May 22, 2026, 1:47 p.m.
NED2 Entity disambiguation (via description) batch_6a105ebce9188190b2bb2874b41008f0 completed May 22, 2026, 1:48 p.m.
Created at: April 18, 2026, 4:48 a.m.