Triple

T785077
Position Surface form Disambiguated ID Type / Status
Subject Bulgarian Orthodox Church E16583 entity
Predicate headquartersLocation P62 FINISHED
Object Sofia E31299 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: Sofia | Statement: [Bulgarian Orthodox Church, headquartersLocation, Sofia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sofia
Context triple: [Bulgarian Orthodox Church, headquartersLocation, Sofia]
  • A. Sofia chosen
    Sofia is the capital and largest city of Bulgaria, known as a major cultural, economic, and historical center in the Balkans.
  • B. Varna
    Varna is a major Bulgarian city on the Black Sea coast known as an important economic, cultural, and maritime center.
  • C. Ruse
    Ruse is a major Bulgarian city and river port on the Danube, known for its elegant architecture and role as an important economic and transport hub.
  • D. Bucharest
    Bucharest is the capital and largest city of Romania, known for its mix of historic architecture, wide boulevards, and its role as the country’s political, cultural, and economic center.
  • E. Belgrade
    Belgrade is the capital and largest city of Serbia, historically significant as a strategic crossroads between Central Europe and the Balkans on the confluence of the Sava and Danube rivers.
  • 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_69a4936ad1fc81908f190208059ccf78 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a76b0d6c8190a09b1a0bd4a6eeec completed March 1, 2026, 8:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7a3afbb0c8190b4e7aa7824c8b787 completed March 4, 2026, 3:14 a.m.
Created at: March 1, 2026, 7:37 p.m.