Triple

T17855668
Position Surface form Disambiguated ID Type / Status
Subject Princess Wilhelmine of Baden E445928 entity
Predicate nobleTitle P914 FINISHED
Object Princess of Baden
Princess of Baden is a noble title historically borne by female members of the Grand Ducal House of Baden, a former sovereign dynasty in what is now southwestern Germany.
E1620707 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 of Baden | Statement: [Princess Wilhelmine of Baden, nobleTitle, Princess of Baden]
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 of Baden
Triple: [Princess Wilhelmine of Baden, nobleTitle, Princess of Baden]
Generated description
Princess of Baden is a noble title historically borne by female members of the Grand Ducal House of Baden, a former sovereign dynasty in what is now southwestern Germany.

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_69d8b9f26f18819089c9e43250bee6ae completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e4978af9b0819091780281344f5352 completed April 19, 2026, 8:51 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0facddecc08190b8054df90457baf3 completed May 22, 2026, 1:09 a.m.
NEDg Description generation batch_6a0fae10893c819092a3ecd95b6b9198 completed May 22, 2026, 1:14 a.m.
NED2 Entity disambiguation (via description) batch_6a0faf345eac8190b8a648c3add470bd completed May 22, 2026, 1:19 a.m.
Created at: April 10, 2026, 10:17 a.m.