Triple

T33044858
Position Surface form Disambiguated ID Type / Status
Subject Sue Ryder E845565 entity
Predicate birthName P65 FINISHED
Object Margaret Susan Ryder
Margaret Susan Ryder, better known as Sue Ryder, was a British humanitarian and philanthropist who founded the Sue Ryder Foundation to provide care for people in need, especially those affected by war and serious illness.
E2058425 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: Margaret Susan Ryder | Statement: [Sue Ryder, birthName, Margaret Susan Ryder]
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: Margaret Susan Ryder
Triple: [Sue Ryder, birthName, Margaret Susan Ryder]
Generated description
Margaret Susan Ryder, better known as Sue Ryder, was a British humanitarian and philanthropist who founded the Sue Ryder Foundation to provide care for people in need, especially those affected by war and serious illness.

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_69f3495242e48190996a2cb2beab5455 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d3144a8c8190ad91f87b4d83fd1b completed May 3, 2026, 4:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a35afb4ab708190a725bce057719f1b completed June 19, 2026, 9:08 p.m.
NEDg Description generation batch_6a35b12f787c8190978b95d0105cebbd completed June 19, 2026, 9:14 p.m.
NED2 Entity disambiguation (via description) batch_6a35b1a8450c81909cdf93e9a4973784 completed June 19, 2026, 9:16 p.m.
Created at: May 1, 2026, 1:24 a.m.