Triple

T31032783
Position Surface form Disambiguated ID Type / Status
Subject Henry Hopkinson, 1st Baron Colyton E790771 entity
Predicate honorificTitle P2097 FINISHED
Object 1st Baron Colyton
1st Baron Colyton was a British Conservative politician and diplomat, Henry Hopkinson, who served in various governmental and colonial administrative roles in the mid-20th century.
E1942179 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: 1st Baron Colyton | Statement: [Henry Hopkinson, 1st Baron Colyton, honorificTitle, 1st Baron Colyton]
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: 1st Baron Colyton
Triple: [Henry Hopkinson, 1st Baron Colyton, honorificTitle, 1st Baron Colyton]
Generated description
1st Baron Colyton was a British Conservative politician and diplomat, Henry Hopkinson, who served in various governmental and colonial administrative roles in the mid-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_69f224c97a788190b5da1ead6038a74e completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f694c2fe008190b643a2b2d8274669 completed May 3, 2026, 12:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a29184c697c8190908c98da09899d55 completed June 10, 2026, 7:54 a.m.
NEDg Description generation batch_6a291920ea2081908db1559b54147427 completed June 10, 2026, 7:58 a.m.
NED2 Entity disambiguation (via description) batch_6a29199ab674819099331e028cf6d811 completed June 10, 2026, 8 a.m.
Created at: April 29, 2026, 8:59 p.m.