Triple

T31678201
Position Surface form Disambiguated ID Type / Status
Subject Ralph Montagu, 1st Duke of Montagu E808461 entity
Predicate spouse P13 FINISHED
Object Elizabeth Cavendish
Elizabeth Cavendish was an English noblewoman of the Cavendish family who became Duchess of Montagu through her marriage into the Montagu dynasty.
E1973197 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: Elizabeth Cavendish | Statement: [Ralph Montagu, 1st Duke of Montagu, spouse, Elizabeth Cavendish]
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: Elizabeth Cavendish
Triple: [Ralph Montagu, 1st Duke of Montagu, spouse, Elizabeth Cavendish]
Generated description
Elizabeth Cavendish was an English noblewoman of the Cavendish family who became Duchess of Montagu through her marriage into the Montagu dynasty.

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_69f348dcf5d48190ac25b1365ae717a8 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6aa53ff6881909cb1a2de983d0832 completed May 3, 2026, 1:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b84b6ecf88190b48186ac07765f45 completed June 12, 2026, 4:01 a.m.
NEDg Description generation batch_6a2b854efcc88190a0ad9b5f2aaecf26 completed June 12, 2026, 4:04 a.m.
NED2 Entity disambiguation (via description) batch_6a2b85ee4f088190a5b413a6b7d0e1b5 completed June 12, 2026, 4:07 a.m.
Created at: April 30, 2026, 11:03 p.m.