Triple

T15234808
Position Surface form Disambiguated ID Type / Status
Subject Yellow Line (Budapest Metro) E364096 entity
Predicate terminus P388 FINISHED
Object Vörösmarty tér E1162768 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: Vörösmarty tér | Statement: [Yellow Line (Budapest Metro), terminus, Vörösmarty tér]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vörösmarty tér
Context triple: [Yellow Line (Budapest Metro), terminus, Vörösmarty tér]
  • A. Vörösmarty tér chosen
    Vörösmarty tér is a prominent central square in Budapest, Hungary, known for its historic cafés, shopping streets, and seasonal markets.
  • B. Rákóczi tér
    Rákóczi tér is a public square and transport hub in Budapest known for its central location and metro station in the Józsefváros district.
  • C. Erzsébet tér
    Erzsébet tér is a central square and popular public park in downtown Budapest, known for its green spaces, cultural venues, and vibrant urban atmosphere.
  • D. Nagyvárad tér
    Nagyvárad tér is a metro station in Budapest that serves the city’s public transportation network on one of its main lines.
  • E. Széchenyi István tér
    Széchenyi István tér is a prominent square in central Budapest, Hungary, known for its grand riverside location by the Danube and its surrounding historic and cultural landmarks.
  • 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_69d85a0ce24c81909c4d3b6475548c95 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007d91e4881908ea52d11a3d4480a completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff82e1b698819098930596327340d7 completed May 9, 2026, 6:54 p.m.
Created at: April 10, 2026, 3:12 a.m.