Triple

T35176245
Position Surface form Disambiguated ID Type / Status
Subject Alfonso de Orleans y Borbón E1015710 entity
Predicate familyName P18 FINISHED
Object de Orleans y Borbón
De Orleans y Borbón is a Spanish royal family surname associated with a cadet branch of the Bourbon dynasty that has held various noble and military roles in Spain.
 E2130425 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: de Orleans y Borbón | Statement: [Alfonso de Orleans y Borbón, familyName, de Orleans y Borbón]
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: de Orleans y Borbón
Triple: [Alfonso de Orleans y Borbón, familyName, de Orleans y Borbón]
Generated description
De Orleans y Borbón is a Spanish royal family surname associated with a cadet branch of the Bourbon dynasty that has held various noble and military roles in Spain.

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_69f76ddcc108819097f96853b7ed9ef4 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78d76f65c81908af6b116e3eedb12 completed May 3, 2026, 6:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3803fc7d14819080fa724ee9e0d3c3 completed June 21, 2026, 3:32 p.m.
NEDg Description generation batch_6a380492146c819091e84a4db90e432f completed June 21, 2026, 3:34 p.m.
NED2 Entity disambiguation (via description) batch_6a38054099408190b0a218ecb7f84dc6 completed June 21, 2026, 3:37 p.m.
Created at: May 3, 2026, 4:02 p.m.