Triple

T24247569
Position Surface form Disambiguated ID Type / Status
Subject Marvyn E603420 entity
Predicate associatedWithFamily P566 FINISHED
Object Marvyn family of Wiltshire
The Marvyn family of Wiltshire was an English gentry lineage historically established in the county of Wiltshire, known for its local influence and landholdings.
E1626635 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: Marvyn family of Wiltshire | Statement: [Marvyn, associatedWithFamily, Marvyn family of Wiltshire]
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: Marvyn family of Wiltshire
Triple: [Marvyn, associatedWithFamily, Marvyn family of Wiltshire]
Generated description
The Marvyn family of Wiltshire was an English gentry lineage historically established in the county of Wiltshire, known for its local influence and landholdings.

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_69e29540da0481909a38bdae315b7a02 completed April 17, 2026, 8:17 p.m.
NER Named-entity recognition batch_69f28b860a0c81908c884ae876b46093 completed April 29, 2026, 10:51 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fbd35bf088190aae000ccc4b22017 completed May 22, 2026, 2:19 a.m.
NEDg Description generation batch_6a0fbee12e748190ac0d28656458a335 completed May 22, 2026, 2:26 a.m.
NED2 Entity disambiguation (via description) batch_6a0fc2b76df88190b6bcba834def7619 completed May 22, 2026, 2:43 a.m.
Created at: April 18, 2026, 12:04 a.m.