Triple

T9224477
Position Surface form Disambiguated ID Type / Status
Subject Aleksei German E221645 entity
Predicate workLocation P7 FINISHED
Object Leningrad 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: Leningrad | Statement: [Aleksei German, workLocation, Leningrad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leningrad
Context triple: [Aleksei German, workLocation, Leningrad]
  • A. 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.
  • B. Kirov Leningrad
    Kirov Leningrad was the historic name of the Soviet-era ice hockey club from Leningrad that later became known as SKA Saint Petersburg.
  • C. Konets Sankt-Peterburga
    Konets Sankt-Peterburga is a 1927 Soviet silent film directed by Vsevolod Pudovkin, renowned as a landmark of montage cinema and revolutionary propaganda.
  • D. Stalino
    Stalino was the Soviet-era name of the industrial city now known as Donetsk in eastern Ukraine.
  • E. Petrovgrad
    Petrovgrad was the former name of the Serbian city now known as Zrenjanin, located in the Vojvodina region.
  • 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_69ca83ec8db08190a9110df8232885d2 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccda9c71c4819089dcc3689f322529 completed April 1, 2026, 8:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0664565748190a45cf382fca72cbe completed April 4, 2026, 1:15 a.m.
Created at: March 30, 2026, 7:28 p.m.