Triple

T2024154
Position Surface form Disambiguated ID Type / Status
Subject David Einhorn E44168 entity
Predicate residence P75 FINISHED
Object Pest, Hungary
Pest is the eastern, urbanized part of Hungary’s capital city Budapest, known as its commercial and administrative center along the banks of the Danube River.
 E225296 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: Pest, Hungary | Statement: [David Einhorn, residence, Pest, Hungary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pest, Hungary
Context triple: [David Einhorn, residence, Pest, Hungary]
  • A. Kaposvár, Hungary
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • B. Tatabánya
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • C. Keszthely
    Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
  • D. Northern Hungary
    Northern Hungary is a region of Hungary known for its industrial cities like Miskolc, historic castles, and the Bükk and Mátra mountain ranges.
  • E. Zamárdi
    Zamárdi is a popular Hungarian resort town on the southern shore of Lake Balaton, known for its beaches, lakeside recreation, and summer festivals.
  • 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: Pest, Hungary
Triple: [David Einhorn, residence, Pest, Hungary]
Generated description
Pest is the eastern, urbanized part of Hungary’s capital city Budapest, known as its commercial and administrative center along the banks of the Danube River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pest, Hungary
Target entity description: Pest is the eastern, urbanized part of Hungary’s capital city Budapest, known as its commercial and administrative center along the banks of the Danube River.
  • A. Kaposvár, Hungary
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • B. Tatabánya
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • C. Keszthely
    Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
  • D. Northern Hungary
    Northern Hungary is a region of Hungary known for its industrial cities like Miskolc, historic castles, and the Bükk and Mátra mountain ranges.
  • E. Zamárdi
    Zamárdi is a popular Hungarian resort town on the southern shore of Lake Balaton, known for its beaches, lakeside recreation, and summer festivals.
  • 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_69a8891201bc8190aca837be6de41579 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb8f2cd5c8190b19da6f6aa2001d6 completed March 7, 2026, 5:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae0afa82ac81908c3e3c60c5721536 completed March 8, 2026, 11:49 p.m.
NEDg Description generation batch_69ae0b8bd2bc8190a6f16519f3f6e924 completed March 8, 2026, 11:51 p.m.
NED2 Entity disambiguation (via description) batch_69ae0bf5364c8190bffbbd211a5e11a6 completed March 8, 2026, 11:53 p.m.
Created at: March 4, 2026, 7:38 p.m.