Triple

T32232897
Position Surface form Disambiguated ID Type / Status
Subject Francis, Duke of Anjou E823388 entity
Predicate positionHeld P8 FINISHED
Object Duke of Alençon
The Duke of Alençon was a French noble title historically held by junior members of the royal family, notably associated with the Valois dynasty during the late Middle Ages and Renaissance.
E1493002 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: Duke of Alençon | Statement: [Francis, Duke of Anjou, positionHeld, Duke of Alençon]
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: Duke of Alençon
Triple: [Francis, Duke of Anjou, positionHeld, Duke of Alençon]
Generated description
The Duke of Alençon was a French noble title historically held by junior members of the royal family, notably associated with the Valois dynasty during the late Middle Ages and Renaissance.

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_69f3490c140481908ed53b98b561eaa1 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bbfcb370819088ba309249ce82f1 completed May 3, 2026, 3:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3542f88f708190b88cf4e5ba317fe8 completed June 19, 2026, 1:24 p.m.
NEDg Description generation batch_6a35448df33881909da08d0e9f0e4320 completed June 19, 2026, 1:30 p.m.
NED2 Entity disambiguation (via description) batch_6a35491ef8d48190bf00c34a28850ce3 completed June 19, 2026, 1:50 p.m.
Created at: May 1, 2026, 12:39 a.m.