Triple

T18449793
Position Surface form Disambiguated ID Type / Status
Subject Anna Leopoldowna E450748 entity
Predicate title P38 FINISHED
Object Duchess of Brunswick-Lüneburg
Anna Leopoldowna was a Russian regent and noblewoman of German origin who briefly ruled the Russian Empire on behalf of her infant son, Emperor Ivan VI.
E2036388 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: Duchess of Brunswick-Lüneburg | Statement: [Anna Leopoldowna, title, Duchess of Brunswick-Lüneburg]
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: Duchess of Brunswick-Lüneburg
Triple: [Anna Leopoldowna, title, Duchess of Brunswick-Lüneburg]
Generated description
Anna Leopoldowna was a Russian regent and noblewoman of German origin who briefly ruled the Russian Empire on behalf of her infant son, Emperor Ivan VI.

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_69d8d38345688190b565eac2e4cd7935 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5264748dc8190984501af3e4b2036 completed April 19, 2026, 7 p.m.
NED1 Entity disambiguation (via context triple) batch_6a34efec3d7c8190a5af3b5d914acd3d completed June 19, 2026, 7:29 a.m.
NEDg Description generation batch_6a350ac932848190984ffface3e890b7 completed June 19, 2026, 9:24 a.m.
NED2 Entity disambiguation (via description) batch_6a350b5d0c44819099aa22dd3156ff2d completed June 19, 2026, 9:26 a.m.
Created at: April 10, 2026, 11:30 a.m.