Triple

T26064085
Position Surface form Disambiguated ID Type / Status
Subject Anna de’ Medici E657350 entity
Predicate motherOf P120 FINISHED
Object Empress Claudia Felicitas
Empress Claudia Felicitas was a 17th-century Holy Roman Empress, the second wife of Emperor Leopold I, known for her brief tenure and early death, which ended hopes for a lasting Tyrolean-Habsburg line through her.
E1708420 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: Empress Claudia Felicitas | Statement: [Anna de’ Medici, motherOf, Empress Claudia Felicitas]
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: Empress Claudia Felicitas
Triple: [Anna de’ Medici, motherOf, Empress Claudia Felicitas]
Generated description
Empress Claudia Felicitas was a 17th-century Holy Roman Empress, the second wife of Emperor Leopold I, known for her brief tenure and early death, which ended hopes for a lasting Tyrolean-Habsburg line through her.

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_69ee5bbd788481909e22bd7153d0c037 completed April 26, 2026, 6:38 p.m.
NER Named-entity recognition batch_69f606954f288190bcb77d432ed3617c completed May 2, 2026, 2:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a111b2a098481909307f13643e81bf6 completed May 23, 2026, 3:12 a.m.
NEDg Description generation batch_6a111cadcd9c819085ea0676070228ad completed May 23, 2026, 3:19 a.m.
NED2 Entity disambiguation (via description) batch_6a111df51a8c8190841ee5f63b0c5633 completed May 23, 2026, 3:24 a.m.
Created at: April 26, 2026, 7:21 p.m.