Triple

T8833249
Position Surface form Disambiguated ID Type / Status
Subject Burgas Province E210197 entity
Predicate hasSeasideResort P10141 FINISHED
Object Primorsko E759993 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: Primorsko | Statement: [Burgas Province, hasSeasideResort, Primorsko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Primorsko
Context triple: [Burgas Province, hasSeasideResort, Primorsko]
  • A. Primorsko chosen
    Primorsko is a Bulgarian Black Sea coastal town and resort known for its beaches and tourism, located in southeastern Bulgaria.
  • B. Primorsk
    Primorsk is a port town in northwestern Russia situated on the coast of the Gulf of Finland in Leningrad Oblast.
  • C. Kraljevica
    Kraljevica is a coastal town in western Croatia known for its historic castles and shipyard on the Adriatic Sea.
  • D. Jurjev
    Jurjev is a historical name for the Estonian city of Tartu, reflecting its past under various regional powers.
  • E. Dubrovno
    Dubrovno is a small town in eastern Belarus, historically part of the Russian Empire and home to a significant Jewish community in the 19th and early 20th centuries.
  • 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_69ca8388549c819095fd94eadefbb007 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc60670fa48190b2a873f6498de7f6 completed April 1, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfab71962c8190823a1ca1d4fa56f3 completed April 3, 2026, 11:58 a.m.
Created at: March 30, 2026, 6:47 p.m.