Triple

T27776080
Position Surface form Disambiguated ID Type / Status
Subject Sir John Saunders E699187 entity
Predicate title P38 FINISHED
Object The Honourable Mr Justice Saunders
The Honourable Mr Justice Saunders is a senior British High Court judge, formally styled to reflect his knighthood and judicial office.
 E1789218 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: The Honourable Mr Justice Saunders | Statement: [Sir John Saunders, title, The Honourable Mr Justice Saunders]
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: The Honourable Mr Justice Saunders
Triple: [Sir John Saunders, title, The Honourable Mr Justice Saunders]
Generated description
The Honourable Mr Justice Saunders is a senior British High Court judge, formally styled to reflect his knighthood and judicial office.

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_69ef6a4b5a9081909c9111396c2be3d2 completed April 27, 2026, 1:53 p.m.
NER Named-entity recognition batch_69f6379ab2248190ac5eadadb9bd5e3b completed May 2, 2026, 5:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12ecbf79f88190bdc3ba06b4d2a790 completed May 24, 2026, 12:19 p.m.
NEDg Description generation batch_6a12ee3f436c8190b1daf7ec5041304e completed May 24, 2026, 12:25 p.m.
NED2 Entity disambiguation (via description) batch_6a12eef54e2c8190b9e8d589f036b066 completed May 24, 2026, 12:28 p.m.
Created at: April 27, 2026, 5:06 p.m.