Triple

T24225291
Position Surface form Disambiguated ID Type / Status
Subject St George's School, Harpenden E601576 entity
Predicate hasNotableAlumni P51 FINISHED
Object James Graham-Brown
James Graham-Brown is a former English cricketer who later became a playwright and author, known for writing under the pseudonym Dougie Blaxland.
E1630051 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: James Graham-Brown | Statement: [St George's School, Harpenden, hasNotableAlumni, James Graham-Brown]
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: James Graham-Brown
Triple: [St George's School, Harpenden, hasNotableAlumni, James Graham-Brown]
Generated description
James Graham-Brown is a former English cricketer who later became a playwright and author, known for writing under the pseudonym Dougie Blaxland.

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_69e29537ca548190b94a37ebe1977caf completed April 17, 2026, 8:16 p.m.
NER Named-entity recognition batch_69f287df14148190ac2dd00bc248ebd2 completed April 29, 2026, 10:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc9b2256081908459c54dddf5bf7b completed May 22, 2026, 3:12 a.m.
NEDg Description generation batch_6a0fcf6a9da08190889bebd86fa3184d completed May 22, 2026, 3:37 a.m.
NED2 Entity disambiguation (via description) batch_6a0fcfd646a0819090262a17c3d05be9 completed May 22, 2026, 3:39 a.m.
Created at: April 18, 2026, midnight