Triple

T1698919
Position Surface form Disambiguated ID Type / Status
Subject Western Ukraine E36723 entity
Predicate hasPart P35 FINISHED
Object Lviv E13495 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: Lviv | Statement: [Western Ukraine, hasPart, Lviv]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lviv
Context triple: [Western Ukraine, hasPart, Lviv]
  • A. Lutsk
    Lutsk is a historic city in northwestern Ukraine, known as the administrative center of Volyn Oblast and one of the region’s oldest cultural and economic hubs.
  • B. Lwów chosen
    Lwów is a historic city in Eastern Europe, now known as Lviv in western Ukraine, long recognized as a major cultural and political center of the region.
  • C. Rzeszów
    Rzeszów is a major city in southeastern Poland known as an important economic, academic, and cultural center of the region.
  • D. Ternopil
    Ternopil is a city in western Ukraine known as a regional cultural and economic center with a historic old town and a picturesque lakeside setting.
  • E. Drohobych
    Drohobych is a historic city in western Ukraine known for its medieval architecture, salt production heritage, and association with writer and artist Bruno Schulz.
  • 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_69a886163dec8190859c514232a37a05 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa62d3c57c81908887844e885062e3 completed March 6, 2026, 5:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae0300207481908c8b91b83b2575bc completed March 8, 2026, 11:15 p.m.
Created at: March 4, 2026, 7:30 p.m.