Triple

T32608820
Position Surface form Disambiguated ID Type / Status
Subject National Socialist Motor Corps E833597 entity
Predicate notableLeader P304 FINISHED
Object Erwin Kraus
Erwin Kraus was a high-ranking Nazi official who served as a notable leader within the National Socialist Motor Corps (NSKK) in Germany during the Third Reich.
E2034916 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: Erwin Kraus | Statement: [National Socialist Motor Corps, notableLeader, Erwin Kraus]
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: Erwin Kraus
Triple: [National Socialist Motor Corps, notableLeader, Erwin Kraus]
Generated description
Erwin Kraus was a high-ranking Nazi official who served as a notable leader within the National Socialist Motor Corps (NSKK) in Germany during the Third Reich.

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_69f3492bfa648190b6ae472074634e29 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6c6c91a1c819097d51d2a159e223b completed May 3, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34e4eea7548190ad545d25913da977 completed June 19, 2026, 6:42 a.m.
NEDg Description generation batch_6a34e654e7288190ae18f37300d8bfb6 completed June 19, 2026, 6:48 a.m.
NED2 Entity disambiguation (via description) batch_6a34e7032cac81909ef52e16456c9a15 completed June 19, 2026, 6:51 a.m.
Created at: May 1, 2026, 1:05 a.m.