Triple

T14864165
Position Surface form Disambiguated ID Type / Status
Subject Rózsadomb E349574 entity
Predicate adjacentTo P224 FINISHED
Object Margit körút
Margit körút is a major boulevard in Budapest, Hungary, known for connecting the Buda side’s central districts and serving as an important traffic and public transport artery near the Danube.
 E1123467 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: Margit körút | Statement: [Rózsadomb, adjacentTo, Margit körút]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Margit körút
Context triple: [Rózsadomb, adjacentTo, Margit körút]
  • A. Margareta
    Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
  • B. Margarida
    Margarida is a given name, commonly used in Portuguese and Catalan, that corresponds to the English name Margaret.
  • C. Majgull
    Majgull is a Swedish given name, notably borne by the acclaimed author Majgull Axelsson.
  • D. Birgitte
    Birgitte is a Danish-born member of the British royal family who holds the title Duchess of Gloucester.
  • E. Magda
    Magda is a feminine given name, commonly used as a short form of Magdalena in various European languages.
  • 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: Margit körút
Triple: [Rózsadomb, adjacentTo, Margit körút]
Generated description
Margit körút is a major boulevard in Budapest, Hungary, known for connecting the Buda side’s central districts and serving as an important traffic and public transport artery near the Danube.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Margit körút
Target entity description: Margit körút is a major boulevard in Budapest, Hungary, known for connecting the Buda side’s central districts and serving as an important traffic and public transport artery near the Danube.
  • A. Margareta
    Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
  • B. Margarida
    Margarida is a given name, commonly used in Portuguese and Catalan, that corresponds to the English name Margaret.
  • C. Majgull
    Majgull is a Swedish given name, notably borne by the acclaimed author Majgull Axelsson.
  • D. Birgitte
    Birgitte is a Danish-born member of the British royal family who holds the title Duchess of Gloucester.
  • E. Magda
    Magda is a feminine given name, commonly used as a short form of Magdalena in various European languages.
  • 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_69d822ed7e1881909b90fca143ad7e34 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded574d0ec8190a6afed672ba6c2f9 completed April 15, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe650e8aec8190acd4a9cb9cad2039 completed May 8, 2026, 10:34 p.m.
NEDg Description generation batch_69fe65ac6a5c81908621dc17edc6b04f completed May 8, 2026, 10:37 p.m.
NED2 Entity disambiguation (via description) batch_69fe6697fe3881908aae42abe56d86f8 completed May 8, 2026, 10:41 p.m.
Created at: April 10, 2026, 1:54 a.m.