Triple

T17286174
Position Surface form Disambiguated ID Type / Status
Subject GBI E419660 entity
Predicate locatedIn P40 FINISHED
Object Berlin — NE ONDG

How this triple was built (4 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 | Statement: [GBI, locatedIn, Berlin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Berlin
Context triple: [GBI, locatedIn, Berlin]
  • A. Berlin
    Berlin is a small town in South Africa’s Eastern Cape province, situated within the Buffalo City Metropolitan Municipality near East London.
  • B. Berlin
    Berlin is the capital and largest city of Germany, historically significant as a focal point of Cold War tensions and a major cultural, political, and economic center in Europe.
  • C. Berlin
    Berlin is a major Ethereum network upgrade that introduced various gas cost optimizations and transaction processing improvements to enhance the blockchain’s efficiency and performance.
  • D. Berlin
    Berlin is a borough in Camden County, New Jersey, known as a suburban community within the Philadelphia metropolitan area.
  • E. Berlin
    Berlin is a charismatic, calculating, and morally ambiguous mastermind and heist leader in the Spanish television series "Money Heist" (La Casa de Papel).
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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
Triple: [GBI, locatedIn, Berlin]
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Berlin
Target entity description: Berlin is the capital and largest city of Germany, renowned for its rich history, cultural diversity, and vibrant arts and nightlife scenes.
  • A. Berlin chosen
    Berlin is the capital and largest city of Germany, historically significant as a focal point of Cold War tensions and a major cultural, political, and economic center in Europe.
  • B. Berlin
    Berlin is a charismatic, calculating, and morally ambiguous mastermind and heist leader in the Spanish television series "Money Heist" (La Casa de Papel).
  • C. Berlin
    Berlin is a major Ethereum network upgrade that introduced various gas cost optimizations and transaction processing improvements to enhance the blockchain’s efficiency and performance.
  • D. Berlin
    Berlin is a borough in Camden County, New Jersey, known as a suburban community within the Philadelphia metropolitan area.
  • E. Berlin
    Berlin is a small town in South Africa’s Eastern Cape province, situated within the Buffalo City Metropolitan Municipality near East London.
  • F. None of above.

Provenance (4 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_69d886db32608190a61e18862c5a8af6 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e4378024988190aac6aec006f8f7a1 completed April 19, 2026, 2:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0195488d2c81908ac6c19f54f61796 completed May 11, 2026, 8:37 a.m.
NEDg Description generation batch_6a01965807cc819088792a88b8a099d3 in_progress May 11, 2026, 8:42 a.m.
Created at: April 10, 2026, 5:40 a.m.