Triple

T28663652
Position Surface form Disambiguated ID Type / Status
Subject Minister Presidents of the Western German Länder E725529 entity
Predicate officeHolder P537 FINISHED
Object Max Brauer
Max Brauer was a prominent German Social Democratic politician best known for serving as First Mayor of Hamburg in the post–World War II era and helping to rebuild the city.
E1827944 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: Max Brauer | Statement: [Minister Presidents of the Western German Länder, officeHolder, Max Brauer]
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: Max Brauer
Triple: [Minister Presidents of the Western German Länder, officeHolder, Max Brauer]
Generated description
Max Brauer was a prominent German Social Democratic politician best known for serving as First Mayor of Hamburg in the post–World War II era and helping to rebuild the city.

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_69f01d85be388190b669a0e401e2f2c4 completed April 28, 2026, 2:37 a.m.
NER Named-entity recognition batch_69f655a20eb8819088aef0134fc6799a completed May 2, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1cc393cf908190a936b23087af8f2a completed May 31, 2026, 11:26 p.m.
NEDg Description generation batch_6a1cc463bda48190bec84f370b367cff completed May 31, 2026, 11:29 p.m.
NED2 Entity disambiguation (via description) batch_6a1cc4ff58dc8190a81f7ad27e6b6fa8 completed May 31, 2026, 11:32 p.m.
Created at: April 28, 2026, 4:59 a.m.