Triple

T14865012
Position Surface form Disambiguated ID Type / Status
Subject Budapest I. kerülete E349593 entity
Predicate mayor P185 FINISHED
Object Gergely Őrsi
Gergely Őrsi is a Hungarian politician serving as the mayor of Budapest’s 1st District.
E1127717 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: Gergely Őrsi | Statement: [Budapest I. kerülete, mayor, Gergely Őrsi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gergely Őrsi
Context triple: [Budapest I. kerülete, mayor, Gergely Őrsi]
  • A. András Nagy
    András Nagy is a Hungarian biologist and stem cell researcher known for his pioneering work in embryonic stem cells and regenerative medicine.
  • B. Istvan Pely
    Istvan Pely is a video game artist and art director best known for his long-time work at Bethesda Game Studios on franchises like Fallout and The Elder Scrolls.
  • C. Bruno Pésery
    Bruno Pésery is a French film producer known for his work on notable art-house and auteur-driven films.
  • D. Gábor Takács-Nagy
    Gábor Takács-Nagy is a Hungarian violinist and conductor, best known as a founding member of the Takács Quartet and for his leadership of prominent European chamber orchestras.
  • E. Toma Erdődy
    Toma Erdődy was a Croatian nobleman and military leader best known for his role in defending Habsburg territories against the Ottoman Empire in the late 16th century.
  • 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: Gergely Őrsi
Triple: [Budapest I. kerülete, mayor, Gergely Őrsi]
Generated description
Gergely Őrsi is a Hungarian politician serving as the mayor of Budapest’s 1st District.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Gergely Őrsi
Target entity description: Gergely Őrsi is a Hungarian politician serving as the mayor of Budapest’s 1st District.
  • A. András Nagy
    András Nagy is a Hungarian biologist and stem cell researcher known for his pioneering work in embryonic stem cells and regenerative medicine.
  • B. Istvan Pely
    Istvan Pely is a video game artist and art director best known for his long-time work at Bethesda Game Studios on franchises like Fallout and The Elder Scrolls.
  • C. Bruno Pésery
    Bruno Pésery is a French film producer known for his work on notable art-house and auteur-driven films.
  • D. Gábor Takács-Nagy
    Gábor Takács-Nagy is a Hungarian violinist and conductor, best known as a founding member of the Takács Quartet and for his leadership of prominent European chamber orchestras.
  • E. Toma Erdődy
    Toma Erdődy was a Croatian nobleman and military leader best known for his role in defending Habsburg territories against the Ottoman Empire in the late 16th century.
  • 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_69ded5761c688190b4477cb081554b51 completed April 15, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe72aad76c8190b024651483d8f9ff completed May 8, 2026, 11:32 p.m.
NEDg Description generation batch_69fe7b078d0c8190ba80b6e96975fd6c completed May 9, 2026, 12:08 a.m.
NED2 Entity disambiguation (via description) batch_69fe7b26519c8190ba81d997e11a999f completed May 9, 2026, 12:09 a.m.
Created at: April 10, 2026, 1:55 a.m.