Triple

T24906096
Position Surface form Disambiguated ID Type / Status
Subject Battle of Spichern E623712 entity
Predicate commander P1061 FINISHED
Object Hugo von Kameke
Hugo von Kameke was a Prussian general of the 19th century who distinguished himself in the Franco-Prussian War and later served as Prussian Minister of War.
 E1783425 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: Hugo von Kameke | Statement: [Battle of Spichern, commander, Hugo von Kameke]
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: Hugo von Kameke
Triple: [Battle of Spichern, commander, Hugo von Kameke]
Generated description
Hugo von Kameke was a Prussian general of the 19th century who distinguished himself in the Franco-Prussian War and later served as Prussian Minister of War.

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_69e2fac797cc8190b30d77f4121099ac completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f4236bc540819096275eb784a08719 completed May 1, 2026, 3:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a12da5bf38481908d247051af42bb60 completed May 24, 2026, 11 a.m.
NEDg Description generation batch_6a12daf3e7948190bb82f9eac6800971 completed May 24, 2026, 11:03 a.m.
NED2 Entity disambiguation (via description) batch_6a12db74542081909ede3d27600fb26b completed May 24, 2026, 11:05 a.m.
Created at: April 18, 2026, 5:27 a.m.