Triple

T2580099
Position Surface form Disambiguated ID Type / Status
Subject April Theses E57068 entity
Predicate placeOfPresentation P373 FINISHED
Object Petrograd E90774 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: Petrograd | Statement: [April Theses, placeOfPresentation, Petrograd]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Petrograd
Context triple: [April Theses, placeOfPresentation, Petrograd]
  • A. Pushkino
    Pushkino is a town in Russia that serves as a suburban residential and industrial center northeast of Moscow.
  • B. Leningrad chosen
    Leningrad, now known as Saint Petersburg, is a major Russian city on the Baltic Sea that served as the imperial capital and endured a devastating World War II siege.
  • C. Moscow
    Moscow is the capital and largest city of Russia, serving as its political, economic, and cultural center.
  • D. Moscow
    Moscow is a fictional character from the Spanish television series "Money Heist" (La Casa de Papel), known as a kind-hearted, blue-collar miner and the father of Denver who participates in the Royal Mint heist.
  • E. Sofya
    Sofya is the Russian given name of Sophia Tolstaya, the wife and muse of novelist Leo Tolstoy.
  • 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_69ab4a4dca6481908c301f8e317396e7 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd3a9fd3c8190a521931e40cd801c completed March 7, 2026, 7:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc6316e848190a843f9fd5b152e9f completed March 10, 2026, 7:20 a.m.
Created at: March 6, 2026, 9:49 p.m.