Triple

T2812814
Position Surface form Disambiguated ID Type / Status
Subject Mariupol E54211 entity
Predicate formerName P65 FINISHED
Object Pavlovsk E233354 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: Pavlovsk | Statement: [Mariupol, formerName, Pavlovsk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pavlovsk
Context triple: [Mariupol, formerName, Pavlovsk]
  • A. Pavlovsk chosen
    Pavlovsk is a historic Russian town near Saint Petersburg, best known for its imperial palace and landscaped park that exemplify neoclassical architecture and design.
  • B. Tsarskoye Selo
    Tsarskoye Selo is a former imperial residence near Saint Petersburg, Russia, famed for its opulent palaces, landscaped parks, and role as a cultural and historical center of the Russian Empire.
  • C. Krylatskoye
    Krylatskoye is a Moscow Metro station serving the Krylatskoye District in western Moscow, Russia.
  • D. Kolomenskaya
    Kolomenskaya is a Moscow Metro station on the Zamoskvoretskaya Line, serving the Kolomenskoye area in the southern part of the city.
  • E. Odintsovo
    Odintsovo is a town in western Russia that serves as an important suburban center just outside Moscow.
  • 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_69ab49de0af08190b3da69683be1e728 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abde4a31d081909377044d5ff791b0 completed March 7, 2026, 8:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69afce9cd4ec81908900ffee0edb70ca completed March 10, 2026, 7:56 a.m.
Created at: March 6, 2026, 9:59 p.m.