Triple

T4593462
Position Surface form Disambiguated ID Type / Status
Subject European Union administrative centres E103550 entity
Predicate hasMember P10 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: [European Union administrative centres, hasMember, Sofia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sofia
Context triple: [European Union administrative centres, hasMember, 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. Sofia
    Sofia is a strong-willed, outspoken woman in Alice Walker’s "The Color Purple," known for her resilience and defiance against oppression.
  • C. Sofia
    Sofia is a feminine given name of Greek origin, widely used in many cultures and commonly associated with the meaning "wisdom."
  • D. Sofya
    Sofya is the Russian given name of Sophia Tolstaya, the wife and muse of novelist Leo Tolstoy.
  • E. Plovdiv
    Plovdiv is Bulgaria’s second-largest city and one of Europe’s oldest continuously inhabited urban centers, known for its Roman amphitheater, Old Town, and rich cultural heritage.
  • 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_69bd43dccaf08190aa89e9991a289719 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd593cf8508190acfc6ddb5716e80a completed March 20, 2026, 2:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfa3f792881908f7d0bc1f09d517e completed March 21, 2026, 1:54 a.m.
Created at: March 20, 2026, 1:11 p.m.