Triple

T30400625
Position Surface form Disambiguated ID Type / Status
Subject Pissarro family E773337 entity
Predicate hasNotableMember P304 FINISHED
Object Hugues Claude Pissarro
Hugues Claude Pissarro is a French painter and art teacher, grandson of Impressionist master Camille Pissarro, known for his landscapes and cityscapes in both traditional and contemporary styles.
E1922926 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: Hugues Claude Pissarro | Statement: [Pissarro family, hasNotableMember, Hugues Claude Pissarro]
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: Hugues Claude Pissarro
Triple: [Pissarro family, hasNotableMember, Hugues Claude Pissarro]
Generated description
Hugues Claude Pissarro is a French painter and art teacher, grandson of Impressionist master Camille Pissarro, known for his landscapes and cityscapes in both traditional and contemporary styles.

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_69f2248facd48190b183c3f3ca6daef7 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68617d5e88190bc09d1ee6437d240 completed May 2, 2026, 11:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2863bf2a9881909def0aa41625622d completed June 9, 2026, 7:04 p.m.
NEDg Description generation batch_6a286573ecb08190bf5b35997bdbf376 completed June 9, 2026, 7:11 p.m.
NED2 Entity disambiguation (via description) batch_6a2865a82c408190a31a59b1bef3f74e completed June 9, 2026, 7:12 p.m.
Created at: April 29, 2026, 8:03 p.m.