Triple

T9533421
Position Surface form Disambiguated ID Type / Status
Subject Park Kultury E229951 entity
Predicate near P350 FINISHED
Object Gorky Park E494520 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: Gorky Park | Statement: [Park Kultury, near, Gorky Park]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gorky Park
Context triple: [Park Kultury, near, Gorky Park]
  • A. Gorky Park chosen
    Gorky Park is a famous central Moscow park known for its recreational facilities, cultural events, and scenic riverside location.
  • B. Gorky Park
    Gorky Park is a 1983 crime thriller film, based on Martin Cruz Smith’s novel, about a Soviet detective investigating a triple murder in Moscow.
  • C. Taganana
    Taganana is a historic coastal village on Tenerife in Spain’s Canary Islands, known for its dramatic cliffs, traditional architecture, and location within the Anaga mountain range.
  • D. Vecherniy Kvartal
    Vecherniy Kvartal is a popular Ukrainian comedy and satirical TV show known for its sketches, political humor, and live performances.
  • E. Moscow Does Not Believe in Tears
    "Moscow Does Not Believe in Tears" is a 1980 Soviet romantic drama film that follows the lives of three women in Moscow over two decades, exploring themes of love, ambition, and social change, and won the Academy Award for Best Foreign Language Film.
  • 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_69ca8479934c81908006d0e6e970ae05 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd98c9fef88190beb291b41ee26066 completed April 1, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c4033c08190a71535b63d86f4df completed April 4, 2026, 5:37 p.m.
Created at: March 30, 2026, 8 p.m.