Triple

T11888682
Position Surface form Disambiguated ID Type / Status
Subject Охотный Ряд E282857 entity
Predicate hasNearbySquare P7888 FINISHED
Object Манежная площадь E953400 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: Манежная площадь | Statement: [Охотный Ряд, hasNearbySquare, Манежная площадь]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Манежная площадь
Context triple: [Охотный Ряд, hasNearbySquare, Манежная площадь]
  • A. Манежная площадь chosen
    Манежная площадь — центральная площадь Москвы рядом с Кремлём и Александровским садом, являющаяся важным историческим, культурным и прогулочным пространством города.
  • B. Соборная площадь
    Соборная площадь — это центральная историческая площадь, обычно расположенная у главного собора города и служащая важным общественным и культурным пространством.
  • C. Сенатская площадь
    Сенатская площадь — это историческая площадь в центре Санкт-Петербурга, известная как место ключевых событий российской истории, включая восстание декабристов.
  • D. Krakonoš Square
    Krakonoš Square is the historic central town square of Trutnov in the Czech Republic, known for its traditional architecture, shops, and cultural events.
  • E. Charles Square
    Charles Square is one of the largest historic squares in Prague, serving as a major public space and transport hub in the city’s New Town.
  • 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_69d6ab2a90b08190a4e818821cc93e6d completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8d3a2860c8190a21af5fcddbd2f1e completed April 10, 2026, 10:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69f45854b27c81909304aee5e612f934 completed May 1, 2026, 7:37 a.m.
Created at: April 8, 2026, 9:44 p.m.