Triple

T9685857
Position Surface form Disambiguated ID Type / Status
Subject Northern Great Plain E234405 entity
Predicate containsCity P294 FINISHED
Object Szolnok E284469 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: Szolnok | Statement: [Northern Great Plain, containsCity, Szolnok]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Szolnok
Context triple: [Northern Great Plain, containsCity, Szolnok]
  • A. Szolnok chosen
    Szolnok is a city in central Hungary known as an important regional industrial and transportation hub along the Tisza River.
  • B. Kaposvár
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • C. Veszprém
    Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
  • D. Szekszárd
    Szekszárd is a historic Hungarian town renowned as one of the country’s leading red wine regions and the administrative center of Tolna County.
  • E. Szombathely
    Szombathely is one of Hungary’s oldest cities, known for its Roman heritage and role as a regional cultural and economic center near the Austrian border.
  • 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_69ca84ca73208190957a900c8543bdcc completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9cd2dab481908e0d3fed28de9d40 completed April 1, 2026, 10:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69d2e4fa938881908253aed52870210d completed April 5, 2026, 10:40 p.m.
Created at: March 30, 2026, 8:16 p.m.