Triple

T32689651
Position Surface form Disambiguated ID Type / Status
Subject Adenauer I cabinet E835819 entity
Predicate member P10 FINISHED
Object Hans Lukaschek
Hans Lukaschek was a German politician and jurist who served as a federal minister in the early years of the Federal Republic of Germany under Chancellor Konrad Adenauer.
E2296599 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: Hans Lukaschek | Statement: [Adenauer I cabinet, member, Hans Lukaschek]
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: Hans Lukaschek
Triple: [Adenauer I cabinet, member, Hans Lukaschek]
Generated description
Hans Lukaschek was a German politician and jurist who served as a federal minister in the early years of the Federal Republic of Germany under Chancellor Konrad Adenauer.

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_69f3493211388190993801216afbc2a7 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6c81937a88190be6a17c72c801a70 completed May 3, 2026, 3:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a82923dfd688190b1b200dcfc8043e2 completed Aug. 17, 2026, 4:46 a.m.
NEDg Description generation batch_6a829294c96c8190ac72e0d3b0399e61 completed Aug. 17, 2026, 4:48 a.m.
NED2 Entity disambiguation (via description) batch_6a8292e753d881909a0b4ddee5542515 completed Aug. 17, 2026, 4:49 a.m.
Created at: May 1, 2026, 1:09 a.m.