Triple

T29905709
Position Surface form Disambiguated ID Type / Status
Subject Greater Berlin E759527 entity
Predicate successor P78 FINISHED
Object Berlin (as a unified city)
Berlin (as a unified city) is the capital and largest city of Germany, renowned as a major political, cultural, and historical center of Europe.
 E5567 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: Berlin (as a unified city) | Statement: [Greater Berlin, successor, Berlin (as a unified city)]
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: Berlin (as a unified city)
Triple: [Greater Berlin, successor, Berlin (as a unified city)]
Generated description
Berlin (as a unified city) is the capital and largest city of Germany, renowned as a major political, cultural, and historical center of Europe.

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_69f224600590819085e148a01c056ef6 completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f677556fb48190be1de257613ec04d completed May 2, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a274c9080f081908fe1f2d6900590f4 completed June 8, 2026, 11:13 p.m.
NEDg Description generation batch_6a274da2b4f08190b54ffb23bd8b28dc completed June 8, 2026, 11:17 p.m.
NED2 Entity disambiguation (via description) batch_6a274e674bbc8190a88d0e74b663574a completed June 8, 2026, 11:21 p.m.
Created at: April 29, 2026, 6:08 p.m.