Triple

T11946147
Position Surface form Disambiguated ID Type / Status
Subject Banski Suhodol E284302 entity
Predicate closestTown P3883 FINISHED
Object Bansko E284306 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: Bansko | Statement: [Banski Suhodol, closestTown, Bansko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bansko
Context triple: [Banski Suhodol, closestTown, Bansko]
  • A. Bansko chosen
    Bansko is a Bulgarian mountain town renowned as one of Eastern Europe’s leading ski and winter sports resorts.
  • B. Smolyan
    Smolyan is a Bulgarian town known as an administrative, cultural, and tourist center in the Rhodope Mountains.
  • C. Sliven
    Sliven is a city in eastern Bulgaria known for its textile industry, historic role in Bulgarian national revival, and location near the eastern Balkan Mountains.
  • D. Asenovgrad
    Asenovgrad is a town in southern Bulgaria known as a gateway to the Rhodope Mountains and a regional center rich in historical and religious landmarks.
  • E. Svishtov
    Svishtov is a town in northern Bulgaria on the Danube River, known for its historical significance, including proximity to the ancient Roman military site of Novae.
  • 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_69d6ab2db38c8190b1f0ed6663ef8ada completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903456ec0819082b8b10755a6b732 completed April 10, 2026, 2:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f63ca1748190aad1610f22c53f7d completed May 2, 2026, 1:03 p.m.
Created at: April 8, 2026, 9:45 p.m.