Triple

T28648260
Position Surface form Disambiguated ID Type / Status
Subject Flora McCrea Eaton, Lady Eaton E725120 entity
Predicate birthName P65 FINISHED
Object Flora McCrea
Flora McCrea was a Canadian nurse who became a prominent Toronto socialite and philanthropist as Lady Eaton, the wife of department store magnate Sir John Craig Eaton.
E1829106 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: Flora McCrea | Statement: [Flora McCrea Eaton, Lady Eaton, birthName, Flora McCrea]
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: Flora McCrea
Triple: [Flora McCrea Eaton, Lady Eaton, birthName, Flora McCrea]
Generated description
Flora McCrea was a Canadian nurse who became a prominent Toronto socialite and philanthropist as Lady Eaton, the wife of department store magnate Sir John Craig Eaton.

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_69f01d8423888190bd2f4e52605bf261 completed April 28, 2026, 2:37 a.m.
NER Named-entity recognition batch_69f652e2c6648190835054ea46026fc1 completed May 2, 2026, 7:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1cc387498c81908d6f49780515ddca completed May 31, 2026, 11:25 p.m.
NEDg Description generation batch_6a1cc42a1b08819092125b1f3d09f2ca completed May 31, 2026, 11:28 p.m.
NED2 Entity disambiguation (via description) batch_6a1cc4e253288190bb4e761d17423cbf completed May 31, 2026, 11:31 p.m.
Created at: April 28, 2026, 4:50 a.m.