Triple

T25455181
Position Surface form Disambiguated ID Type / Status
Subject Provender House E637894 entity
Predicate notableResident P1092 FINISHED
Object Princess Olga Romanoff
Princess Olga Romanoff is a British-based Russian princess and descendant of the Romanov imperial family, known for her public role in preserving and representing her family's historical legacy.
E1765596 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: Princess Olga Romanoff | Statement: [Provender House, notableResident, Princess Olga Romanoff]
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: Princess Olga Romanoff
Triple: [Provender House, notableResident, Princess Olga Romanoff]
Generated description
Princess Olga Romanoff is a British-based Russian princess and descendant of the Romanov imperial family, known for her public role in preserving and representing her family's historical legacy.

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_69e75db7c5048190b8da9cd7eeedb610 completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f725e4d08190b0304e45a417193b completed May 2, 2026, 1:07 p.m.
NED1 Entity disambiguation (via context triple) batch_6a129c774d908190b4bc89098298c948 completed May 24, 2026, 6:36 a.m.
NEDg Description generation batch_6a129d440e448190bad8b3e249c41698 completed May 24, 2026, 6:40 a.m.
NED2 Entity disambiguation (via description) batch_6a129dac5d2081908e48a30357a8547f completed May 24, 2026, 6:41 a.m.
Created at: April 21, 2026, 2:04 p.m.