Triple

T30800843
Position Surface form Disambiguated ID Type / Status
Subject David Teniers the Elder E784364 entity
Predicate spouse P13 FINISHED
Object Dymphna de Wilde
Dymphna de Wilde was the wife of Flemish painter David Teniers the Elder, connected to the prominent Teniers artistic family of 17th-century Flanders.
 E1932316 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: Dymphna de Wilde | Statement: [David Teniers the Elder, spouse, Dymphna de Wilde]
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: Dymphna de Wilde
Triple: [David Teniers the Elder, spouse, Dymphna de Wilde]
Generated description
Dymphna de Wilde was the wife of Flemish painter David Teniers the Elder, connected to the prominent Teniers artistic family of 17th-century Flanders.

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_69f224b3a7ec819096939414d103e31e completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f6903a89c0819085dec72c68a5443d completed May 3, 2026, midnight
NED1 Entity disambiguation (via context triple) batch_6a28b0b19c148190b0a248e5a0a576fe completed June 10, 2026, 12:32 a.m.
NEDg Description generation batch_6a28b1ed9b0c8190986159e58593fadf completed June 10, 2026, 12:38 a.m.
NED2 Entity disambiguation (via description) batch_6a28b2b2c9fc8190af8deaeceb76e5f9 completed June 10, 2026, 12:41 a.m.
Created at: April 29, 2026, 8:42 p.m.