Triple

T35634304
Position Surface form Disambiguated ID Type / Status
Subject Baron Joseph of Portsoken in the City of London E1029676 entity
Predicate heldBy P8 FINISHED
Object Keith Joseph
Keith Joseph was a prominent British Conservative politician and key intellectual architect of Thatcherism, serving in several senior ministerial roles during the late 20th century.
E79865 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: Keith Joseph | Statement: [Baron Joseph of Portsoken in the City of London, heldBy, Keith Joseph]
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: Keith Joseph
Triple: [Baron Joseph of Portsoken in the City of London, heldBy, Keith Joseph]
Generated description
Keith Joseph was a prominent British Conservative politician and key intellectual architect of Thatcherism, serving in several senior ministerial roles during the late 20th century.

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_69f76e07bb0c8190968ea2d836fc42c9 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f79f1ba6a081908d06ed63032722e5 completed May 3, 2026, 7:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a387d013860819080c90017c6a2be15 completed June 22, 2026, 12:08 a.m.
NEDg Description generation batch_6a387df5525c8190a56a0657210b9c99 completed June 22, 2026, 12:12 a.m.
NED2 Entity disambiguation (via description) batch_6a387e68ab908190bf3f19981dfe0397 completed June 22, 2026, 12:14 a.m.
Created at: May 3, 2026, 4:05 p.m.