Triple

T4099291
Position Surface form Disambiguated ID Type / Status
Subject Western Transdanubia E87898 entity
Predicate hasHistoricalTown P43430 FINISHED
Object Szombathely E421565 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: Szombathely | Statement: [Western Transdanubia, hasHistoricalTown, Szombathely]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Szombathely
Context triple: [Western Transdanubia, hasHistoricalTown, Szombathely]
  • A. Szombathely chosen
    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.
  • B. Sátoraljaújhely
    Sátoraljaújhely is a historic town in northeastern Hungary near the Slovak border, known for its wine region, cultural heritage, and scenic Zemplén Mountains setting.
  • C. Zalaegerszeg
    Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
  • D. Veszprém
    Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
  • E. Kaposvár
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • 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_69aed94564cc8190a9c1457daedb6e7f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af019af25481909e9f1d171356f3e8 completed March 9, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d04bc33c819082cf79f4445610f1 completed March 14, 2026, 9:16 p.m.
Created at: March 9, 2026, 3:40 p.m.