Triple

T14888391
Position Surface form Disambiguated ID Type / Status
Subject Budapest Metro Line 3 E359687 entity
Predicate hasStation P35 FINISHED
Object Határ út
Határ út is a metro station in Budapest, Hungary, serving passengers on the city's M3 (blue) line.
 E1126167 NE FINISHED

How this triple was built (4 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: Határ út | Statement: [Budapest Metro Line 3, hasStation, Határ út]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Határ út
Context triple: [Budapest Metro Line 3, hasStation, Határ út]
  • A. كتاب الحدود
    كتاب الحدود هو مؤلَّف إسلامي كلاسيكي يتناول أحكام الحدود والعقوبات الشرعية في الفقه الإسلامي.
  • B. Wegmarken
    Wegmarken is a collection of Martin Heidegger’s later philosophical writings that mark key stages in the development of his thought.
  • C. Law of the Journey
    Law of the Journey is a monumental inflatable boat sculpture by Ai Weiwei that powerfully addresses the global refugee crisis and human displacement.
  • D. The Border
    The Border is a Finnish historical drama film scored by composer Tuomas Kantelinen, set in the turbulent aftermath of the Finnish Civil War.
  • E. The Border
    The Border is a 1982 crime drama film starring Jack Nicholson as a corrupt U.S. Border Patrol agent confronting moral dilemmas along the U.S.–Mexico border.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Határ út
Triple: [Budapest Metro Line 3, hasStation, Határ út]
Generated description
Határ út is a metro station in Budapest, Hungary, serving passengers on the city's M3 (blue) line.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Határ út
Target entity description: Határ út is a metro station in Budapest, Hungary, serving passengers on the city's M3 (blue) line.
  • A. كتاب الحدود
    كتاب الحدود هو مؤلَّف إسلامي كلاسيكي يتناول أحكام الحدود والعقوبات الشرعية في الفقه الإسلامي.
  • B. Wegmarken
    Wegmarken is a collection of Martin Heidegger’s later philosophical writings that mark key stages in the development of his thought.
  • C. Law of the Journey
    Law of the Journey is a monumental inflatable boat sculpture by Ai Weiwei that powerfully addresses the global refugee crisis and human displacement.
  • D. The Border
    The Border is a Finnish historical drama film scored by composer Tuomas Kantelinen, set in the turbulent aftermath of the Finnish Civil War.
  • E. The Border
    The Border is a 1982 crime drama film starring Jack Nicholson as a corrupt U.S. Border Patrol agent confronting moral dilemmas along the U.S.–Mexico border.
  • F. None of above. chosen

Provenance (5 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_69d827980cbc8190a0c569ae3940a1d9 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69ded5f6cf5c8190b6b28f58fafe5d59 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe6b5f22c08190a9530cbd78cfc801 completed May 8, 2026, 11:01 p.m.
NEDg Description generation batch_69fe6f9b33748190aee0c27879866ca1 completed May 8, 2026, 11:19 p.m.
NED2 Entity disambiguation (via description) batch_69fe703e8c28819081b7bfe638a2202e completed May 8, 2026, 11:22 p.m.
Created at: April 10, 2026, 2:08 a.m.