Triple

T10309581
Position Surface form Disambiguated ID Type / Status
Subject Hajdú-Bihar County E241851 entity
Predicate containsCity P294 FINISHED
Object Hajdúszoboszló E828685 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: Hajdúszoboszló | Statement: [Hajdú-Bihar County, containsCity, Hajdúszoboszló]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hajdúszoboszló
Context triple: [Hajdú-Bihar County, containsCity, Hajdúszoboszló]
  • A. Hajdúszoboszló chosen
    Hajdúszoboszló is a Hungarian spa town renowned for its thermal baths and large water park, making it a major health and wellness tourism destination.
  • B. Dombóvár
    Dombóvár is a town in southern Hungary known as an important local transport and economic center within Tolna County.
  • C. Hajdúböszörmény
    Hajdúböszörmény is a historic town in eastern Hungary known for its hajdú (heyduck) heritage and spacious, uniquely circular urban layout.
  • D. 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.
  • E. Hódmezővásárhely
    Hódmezővásárhely is a city in southeastern Hungary known for its agricultural traditions, pottery, and regional cultural heritage.
  • 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_69d381ac38808190a8ca7457c85b625b completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d32a18ac81909b4efd8c1ba3e113 completed April 7, 2026, 9:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69f63ed654848190b53e8b64d81eb143 completed May 2, 2026, 6:13 p.m.
Created at: April 6, 2026, 11:47 a.m.