Triple

T28728244
Position Surface form Disambiguated ID Type / Status
Subject Heinrich von Brühl E730283 entity
Predicate mother P120 FINISHED
Object Ernestine von der Heyde
Ernestine von der Heyde was a German noblewoman best known as the mother of the influential Saxon statesman Heinrich von Brühl.
 E1836927 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: Ernestine von der Heyde | Statement: [Heinrich von Brühl, mother, Ernestine von der Heyde]
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: Ernestine von der Heyde
Triple: [Heinrich von Brühl, mother, Ernestine von der Heyde]
Generated description
Ernestine von der Heyde was a German noblewoman best known as the mother of the influential Saxon statesman Heinrich von Brühl.

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_69f043e91fe48190b73bcd8e08d433e0 completed April 28, 2026, 5:21 a.m.
NER Named-entity recognition batch_69f6576569bc8190b0eb1f0fde785c14 completed May 2, 2026, 7:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24bb8f8588819086cf2eb4db2b18d9 completed June 7, 2026, 12:30 a.m.
NEDg Description generation batch_6a24c0054a1c8190bcfd93bbe412553b completed June 7, 2026, 12:49 a.m.
NED2 Entity disambiguation (via description) batch_6a24c6b4cdb88190b7421c5ba080fb45 completed June 7, 2026, 1:17 a.m.
Created at: April 28, 2026, 5:57 a.m.