Triple

T34409652
Position Surface form Disambiguated ID Type / Status
Subject Bedingfeld family E883218 entity
Predicate hasMember P10 FINISHED
Object Frances Bedingfeld
Frances Bedingfeld was an English Catholic nun and educator, notable as an early leader of the Institute of the Blessed Virgin Mary (the Bar Convent) in York during a period of religious persecution.
E2095540 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: Frances Bedingfeld | Statement: [Bedingfeld family, hasMember, Frances Bedingfeld]
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: Frances Bedingfeld
Triple: [Bedingfeld family, hasMember, Frances Bedingfeld]
Generated description
Frances Bedingfeld was an English Catholic nun and educator, notable as an early leader of the Institute of the Blessed Virgin Mary (the Bar Convent) in York during a period of religious persecution.

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_69f349c1f2208190a09a489bb8b2719d completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f718bf8c38819087f287b5ea01f4d4 completed May 3, 2026, 9:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a370dcfa2008190bd89a3b1c2843ca3 completed June 20, 2026, 10:01 p.m.
NEDg Description generation batch_6a370e8004088190845998d791ce25b5 completed June 20, 2026, 10:04 p.m.
NED2 Entity disambiguation (via description) batch_6a370eecef8081909865c633fad36302 completed June 20, 2026, 10:06 p.m.
Created at: May 1, 2026, 1:59 a.m.