Triple

T30246907
Position Surface form Disambiguated ID Type / Status
Subject Louise Blanchard Bethune E769079 entity
Predicate spouse P13 FINISHED
Object Robert Armour Bethune
Robert Armour Bethune was an American architect best known as the husband and professional partner of pioneering woman architect Louise Blanchard Bethune in their Buffalo-based practice.
E1905695 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: Robert Armour Bethune | Statement: [Louise Blanchard Bethune, spouse, Robert Armour Bethune]
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: Robert Armour Bethune
Triple: [Louise Blanchard Bethune, spouse, Robert Armour Bethune]
Generated description
Robert Armour Bethune was an American architect best known as the husband and professional partner of pioneering woman architect Louise Blanchard Bethune in their Buffalo-based practice.

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_69f224831dc08190b2e569b987264057 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68076663c81909007e32c28266d12 completed May 2, 2026, 10:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a276453c8288190a50e7cb44ea63a0a completed June 9, 2026, 12:54 a.m.
NEDg Description generation batch_6a276541b1e08190bc9b9cb55f3743c7 completed June 9, 2026, 12:58 a.m.
NED2 Entity disambiguation (via description) batch_6a27661767f081909e0291186c5d6778 completed June 9, 2026, 1:02 a.m.
Created at: April 29, 2026, 7:39 p.m.