Triple

T26217946
Position Surface form Disambiguated ID Type / Status
Subject Catherine of Foix E655680 entity
Predicate spouse P13 FINISHED
Object John III of Albret
John III of Albret was King of Navarre in the late 15th and early 16th centuries, ruling jointly with his wife Catherine of Foix and contending with Spanish expansion that ultimately cost them much of their kingdom.
E1715308 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: John III of Albret | Statement: [Catherine of Foix, spouse, John III of Albret]
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: John III of Albret
Triple: [Catherine of Foix, spouse, John III of Albret]
Generated description
John III of Albret was King of Navarre in the late 15th and early 16th centuries, ruling jointly with his wife Catherine of Foix and contending with Spanish expansion that ultimately cost them much of their kingdom.

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_69ee5b4a77e08190bfcb5f8ecdc55abd completed April 26, 2026, 6:36 p.m.
NER Named-entity recognition batch_69f60d1cd19081909f7575479d6b91ca completed May 2, 2026, 2:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11858f71f48190977ae6823c00d573 completed May 23, 2026, 10:46 a.m.
NEDg Description generation batch_6a11863d1c3881909b35d2859710d956 completed May 23, 2026, 10:49 a.m.
NED2 Entity disambiguation (via description) batch_6a11871f0f9c81908b836c8d759bf8dc completed May 23, 2026, 10:53 a.m.
Created at: April 26, 2026, 8:55 p.m.