Triple

T25393533
Position Surface form Disambiguated ID Type / Status
Subject Ippolita Maria Sforza E636229 entity
Predicate associatedWith P37 FINISHED
Object Court of Naples
The Court of Naples was a prominent Renaissance royal court in southern Italy, known for its political influence, cultural patronage, and role as a center of art, literature, and humanist learning.
E1674826 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: Court of Naples | Statement: [Ippolita Maria Sforza, associatedWith, Court of Naples]
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: Court of Naples
Triple: [Ippolita Maria Sforza, associatedWith, Court of Naples]
Generated description
The Court of Naples was a prominent Renaissance royal court in southern Italy, known for its political influence, cultural patronage, and role as a center of art, literature, and humanist learning.

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_69e75db263888190b77fff9e2827b9a2 completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f56572b9288190abaf921c761fd843 completed May 2, 2026, 2:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a107616fc24819097d820896841a3ae completed May 22, 2026, 3:28 p.m.
NEDg Description generation batch_6a107704fc988190ad63a0cf42ab9278 completed May 22, 2026, 3:32 p.m.
NED2 Entity disambiguation (via description) batch_6a1077935d948190b863fda3fdd47965 completed May 22, 2026, 3:34 p.m.
Created at: April 21, 2026, 1:49 p.m.