Triple

T37357349
Position Surface form Disambiguated ID Type / Status
Subject Gisela Agnes of Rath E927489 entity
Predicate hasChild P369 FINISHED
Object Augustus Louis of Anhalt-Köthen
Augustus Louis of Anhalt-Köthen was an 18th-century German prince of the House of Ascania who ruled the principality of Anhalt-Köthen.
E2231525 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: Augustus Louis of Anhalt-Köthen | Statement: [Gisela Agnes of Rath, hasChild, Augustus Louis of Anhalt-Köthen]
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: Augustus Louis of Anhalt-Köthen
Triple: [Gisela Agnes of Rath, hasChild, Augustus Louis of Anhalt-Köthen]
Generated description
Augustus Louis of Anhalt-Köthen was an 18th-century German prince of the House of Ascania who ruled the principality of Anhalt-Köthen.

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_69f76eb701788190b40824bc4594d985 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb5bc58c1881908a6b9ed9649bf269 completed May 6, 2026, 3:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40951e12848190b7ab9e5c9ffe9da7 completed June 28, 2026, 3:29 a.m.
NEDg Description generation batch_6a4096a06cd881908c727b9134edb207 completed June 28, 2026, 3:36 a.m.
NED2 Entity disambiguation (via description) batch_6a409a56d8cc81909572b61b90dba241 completed June 28, 2026, 3:51 a.m.
Created at: May 3, 2026, 4:16 p.m.