Triple

T24239331
Position Surface form Disambiguated ID Type / Status
Subject Duesenberg E603176 entity
Predicate foundedBy P104 FINISHED
Object August Duesenberg
August Duesenberg was a pioneering German-American automobile engineer and co-founder of the luxury performance car manufacturer Duesenberg, renowned for its advanced engineering and racing success in the early 20th century.
E1628644 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: August Duesenberg | Statement: [Duesenberg, foundedBy, August Duesenberg]
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: August Duesenberg
Triple: [Duesenberg, foundedBy, August Duesenberg]
Generated description
August Duesenberg was a pioneering German-American automobile engineer and co-founder of the luxury performance car manufacturer Duesenberg, renowned for its advanced engineering and racing success in the early 20th century.

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_69e2953f631c819097cbb421046bd417 completed April 17, 2026, 8:17 p.m.
NER Named-entity recognition batch_69f28a9eb68c81908a8293c00e581b41 completed April 29, 2026, 10:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc9b65b788190867246665dded500 completed May 22, 2026, 3:12 a.m.
NEDg Description generation batch_6a0fcccff9e88190a98d2037d4e781fc completed May 22, 2026, 3:26 a.m.
NED2 Entity disambiguation (via description) batch_6a0fcd80ae2c81909ab307688454443b completed May 22, 2026, 3:29 a.m.
Created at: April 18, 2026, 12:03 a.m.