Triple

T10182118
Position Surface form Disambiguated ID Type / Status
Subject Bicske E236811 entity
Predicate hasRoadConnectionTo P11435 FINISHED
Object Tatabánya E161753 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: Tatabánya | Statement: [Bicske, hasRoadConnectionTo, Tatabánya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tatabánya
Context triple: [Bicske, hasRoadConnectionTo, Tatabánya]
  • A. Tatabánya chosen
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • B. Törökbálint
    Törökbálint is a town in Pest County, Hungary, located just southwest of Budapest and known as a suburban residential area with growing commercial and industrial zones.
  • C. Tiszaújváros
    Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
  • D. Dunakeszi
    Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
  • E. Dombóvár
    Dombóvár is a town in southern Hungary known as an important local transport and economic center within Tolna 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_69ca84d7260c8190bfbec36762943f37 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cded32b91c8190b01ad37b2456080a completed April 2, 2026, 4:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69f61e2fa3bc81909edef00c61265c55 completed May 2, 2026, 3:54 p.m.
Created at: March 30, 2026, 9:12 p.m.