Triple

T34253757
Position Surface form Disambiguated ID Type / Status
Subject Lord Kimberley E878814 entity
Predicate child P120 FINISHED
Object John Wodehouse, 2nd Earl of Kimberley
John Wodehouse, 2nd Earl of Kimberley was a British Liberal politician and peer who served in several senior government positions, including Secretary of State for India and the Colonies, in the late 19th and early 20th centuries.
E2089564 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 Wodehouse, 2nd Earl of Kimberley | Statement: [Lord Kimberley, child, John Wodehouse, 2nd Earl of Kimberley]
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 Wodehouse, 2nd Earl of Kimberley
Triple: [Lord Kimberley, child, John Wodehouse, 2nd Earl of Kimberley]
Generated description
John Wodehouse, 2nd Earl of Kimberley was a British Liberal politician and peer who served in several senior government positions, including Secretary of State for India and the Colonies, in the late 19th and early 20th centuries.

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_69f349b421cc8190b4b4655e1d612548 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f712a424e48190be8513664fb82e5a completed May 3, 2026, 9:17 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36e61e6a488190963d23a8a2882782 completed June 20, 2026, 7:12 p.m.
NEDg Description generation batch_6a36e9e0bcc08190ac6375ff71b804f9 completed June 20, 2026, 7:28 p.m.
NED2 Entity disambiguation (via description) batch_6a36ea505ea08190be3e3b20e4207793 completed June 20, 2026, 7:30 p.m.
Created at: May 1, 2026, 1:56 a.m.