Triple

T25737784
Position Surface form Disambiguated ID Type / Status
Subject Sophia Hedwig of Brunswick-Lüneburg E648122 entity
Predicate sibling P363 FINISHED
Object Clara of Brunswick-Lüneburg
Clara of Brunswick-Lüneburg was a German noblewoman of the House of Welf, a Brunswick-Lüneburg princess active in the late 16th and early 17th centuries.
E2290957 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: Clara of Brunswick-Lüneburg | Statement: [Sophia Hedwig of Brunswick-Lüneburg, sibling, Clara of Brunswick-Lüneburg]
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: Clara of Brunswick-Lüneburg
Triple: [Sophia Hedwig of Brunswick-Lüneburg, sibling, Clara of Brunswick-Lüneburg]
Generated description
Clara of Brunswick-Lüneburg was a German noblewoman of the House of Welf, a Brunswick-Lüneburg princess active in the late 16th and early 17th centuries.

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_69e7ab306eec8190b05c312c6ab186b8 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f5fd1726708190ab4382bf35e2db90 completed May 2, 2026, 1:33 p.m.
NED1 Entity disambiguation (via context triple) batch_6a5c1469328081908d598fa6683bdd70 completed July 19, 2026, 12:03 a.m.
NEDg Description generation batch_6a5c160252c88190b14e0764a3884494 completed July 19, 2026, 12:10 a.m.
NED2 Entity disambiguation (via description) batch_6a5c164aa3ec8190b001b9dad1e3baaa completed July 19, 2026, 12:11 a.m.
Created at: April 22, 2026, 3:36 a.m.