Triple

T35541581
Position Surface form Disambiguated ID Type / Status
Subject René Edward De Russy E1027075 entity
Predicate familyName P18 FINISHED
Object De Russy
De Russy is a French-origin surname notably associated with several 19th-century American military officers and engineers.
 E2159053 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: De Russy | Statement: [René Edward De Russy, familyName, De Russy]
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: De Russy
Triple: [René Edward De Russy, familyName, De Russy]
Generated description
De Russy is a French-origin surname notably associated with several 19th-century American military officers and engineers.

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_69f76e008ba08190927acd8e5e0344c8 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f79806ab6c81909cddc30ac63ca80e completed May 3, 2026, 6:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38a4d0d1048190bfd0d338df87293d completed June 22, 2026, 2:58 a.m.
NEDg Description generation batch_6a38a5be38a88190a389bb6a60b2d33b completed June 22, 2026, 3:02 a.m.
NED2 Entity disambiguation (via description) batch_6a38a61805e081909aab709bf28025ef completed June 22, 2026, 3:03 a.m.
Created at: May 3, 2026, 4:04 p.m.