Triple

T36247484
Position Surface form Disambiguated ID Type / Status
Subject Duchy of Chevreuse E891703 entity
Predicate associatedWithPerson P37 FINISHED
Object Claude de Lorraine
Claude de Lorraine was a 16th-century French nobleman and military leader, best known as the first Duke of Guise and a prominent figure in the early French Wars of Religion.
E557819 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: Claude de Lorraine | Statement: [Duchy of Chevreuse, associatedWithPerson, Claude de Lorraine]
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: Claude de Lorraine
Triple: [Duchy of Chevreuse, associatedWithPerson, Claude de Lorraine]
Generated description
Claude de Lorraine was a 16th-century French nobleman and military leader, best known as the first Duke of Guise and a prominent figure in the early French Wars of Religion.

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_69f76e44993481908fa75e4c48d0aab3 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b5d4545881908e3b2865004444a1 completed May 3, 2026, 8:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39b41ded7c81908a8ed6fcb7673e10 completed June 22, 2026, 10:15 p.m.
NEDg Description generation batch_6a39b6f5241481909e49d882c5e51b22 completed June 22, 2026, 10:28 p.m.
NED2 Entity disambiguation (via description) batch_6a39b87500508190824552f54c2a5724 completed June 22, 2026, 10:34 p.m.
Created at: May 3, 2026, 4:09 p.m.