Triple

T12407666
Position Surface form Disambiguated ID Type / Status
Subject Berat Albayrak E296428 entity
Predicate givenName P17 FINISHED
Object Berat E422594 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: Berat | Statement: [Berat Albayrak, givenName, Berat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berat
Context triple: [Berat Albayrak, givenName, Berat]
  • A. Berat chosen
    Berat is a historic city in central Albania renowned for its well-preserved Ottoman architecture and hillside houses, earning it the nickname "the city of a thousand windows" and recognition as a UNESCO World Heritage Site.
  • B. Waase
    Waase is a small village on the island of Ummanz in the German state of Mecklenburg-Vorpommern.
  • C. Bara
    Bara is a town in Pakistan’s Khyber District, known as a key settlement in the Khyber Pass region with strategic and commercial significance.
  • D. Naju
    Naju is a historic city in South Korea known for its pear cultivation and location in the southwestern province of South Jeolla.
  • E. Serua
    Serua is a small volcanic island in Indonesia’s Banda Sea, known for its steep terrain, active geology, and remote location within the Banda Arc.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6ad9f464c81909db36d7e96e34b9e completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d4a08e0819085c656e35038e6b2 completed April 10, 2026, 7:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63488cac08190a81b2151c827932e completed May 2, 2026, 5:29 p.m.
Created at: April 8, 2026, 9:55 p.m.