Triple

T27634053
Position Surface form Disambiguated ID Type / Status
Subject Magistrat bzw. Senat von Berlin E696423 entity
Predicate hasPart P35 FINISHED
Object Senatsverwaltungen
Senatsverwaltungen are the specialized administrative departments of the Berlin Senate responsible for managing distinct policy areas such as education, finance, or urban development within the city-state’s government.
E1781453 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: Senatsverwaltungen | Statement: [Magistrat bzw. Senat von Berlin, hasPart, Senatsverwaltungen]
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: Senatsverwaltungen
Triple: [Magistrat bzw. Senat von Berlin, hasPart, Senatsverwaltungen]
Generated description
Senatsverwaltungen are the specialized administrative departments of the Berlin Senate responsible for managing distinct policy areas such as education, finance, or urban development within the city-state’s government.

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_69ef5909f3848190805f35b76833e722 completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f63126f3e08190a32c02b24aae3c7c completed May 2, 2026, 5:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12d0fb09408190a65a813b69c90da5 completed May 24, 2026, 10:20 a.m.
NEDg Description generation batch_6a12d18c931081909d1620e19d46e1c8 completed May 24, 2026, 10:23 a.m.
NED2 Entity disambiguation (via description) batch_6a12d290bc5081909bd6c027b8a5b4d4 completed May 24, 2026, 10:27 a.m.
Created at: April 27, 2026, 2:22 p.m.