Triple

T31729507
Position Surface form Disambiguated ID Type / Status
Subject Carrara family of Padua E809819 entity
Predicate notableMember P10 FINISHED
Object Marsilio da Carrara
Marsilio da Carrara was a 14th-century Italian nobleman and ruler of Padua, belonging to the powerful Carrara (Carraresi) dynasty that dominated the city’s politics.
E1977809 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: Marsilio da Carrara | Statement: [Carrara family of Padua, notableMember, Marsilio da Carrara]
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: Marsilio da Carrara
Triple: [Carrara family of Padua, notableMember, Marsilio da Carrara]
Generated description
Marsilio da Carrara was a 14th-century Italian nobleman and ruler of Padua, belonging to the powerful Carrara (Carraresi) dynasty that dominated the city’s politics.

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_69f348e0e4908190a884582eca646fb7 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6ab1d46a4819098cbd250564c7d85 completed May 3, 2026, 1:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2d9d4720d881909a60ea3ebd6be6dc completed June 13, 2026, 6:11 p.m.
NEDg Description generation batch_6a2d9df2b8bc81909145216bf1bea8f6 completed June 13, 2026, 6:14 p.m.
NED2 Entity disambiguation (via description) batch_6a2d9eb095408190a454afb237e14476 completed June 13, 2026, 6:17 p.m.
Created at: April 30, 2026, 11:21 p.m.