Triple

T22217113
Position Surface form Disambiguated ID Type / Status
Subject Vilma Bánky E549103 entity
Predicate familyName P18 FINISHED
Object Bánky
Bánky is a Hungarian surname most famously borne by silent film actress Vilma Bánky.
 E1526367 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: Bánky | Statement: [Vilma Bánky, familyName, Bánky]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bánky
Context triple: [Vilma Bánky, familyName, Bánky]
  • A. Baníkov
    Baníkov is a prominent peak in Slovakia’s Western Tatras, popular with hikers for its rugged ridges and panoramic alpine views.
  • B. Hlubočky
    Hlubočky is a municipality and village in the Olomouc Region of the Czech Republic, known historically for its industrial facilities and proximity to the city of Olomouc.
  • C. Ráckeve
    Ráckeve is a historic town in central Hungary situated along the Danube River, known for its Serbian cultural heritage and baroque architecture.
  • D. Hrádeček
    Hrádeček is a small rural settlement in the Czech Republic best known as the longtime country residence and retreat of playwright and former president Václav Havel, where he also died.
  • E. Hrebienok
    Hrebienok is a popular mountain tourist resort and trailhead in the High Tatras of Slovakia, known for its easy cable car access and hiking routes.
  • 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: Bánky
Triple: [Vilma Bánky, familyName, Bánky]
Generated description
Bánky is a Hungarian surname most famously borne by silent film actress Vilma Bánky.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bánky
Target entity description: Bánky is a Hungarian surname most famously borne by silent film actress Vilma Bánky.
  • A. Baníkov
    Baníkov is a prominent peak in Slovakia’s Western Tatras, popular with hikers for its rugged ridges and panoramic alpine views.
  • B. Hlubočky
    Hlubočky is a municipality and village in the Olomouc Region of the Czech Republic, known historically for its industrial facilities and proximity to the city of Olomouc.
  • C. Ráckeve
    Ráckeve is a historic town in central Hungary situated along the Danube River, known for its Serbian cultural heritage and baroque architecture.
  • D. Hrádeček
    Hrádeček is a small rural settlement in the Czech Republic best known as the longtime country residence and retreat of playwright and former president Václav Havel, where he also died.
  • E. Hrebienok
    Hrebienok is a popular mountain tourist resort and trailhead in the High Tatras of Slovakia, known for its easy cable car access and hiking routes.
  • 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_69e11e3f7e04819089806d81d5ac431e completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f12b8c9d488190a59f571862997304 completed April 28, 2026, 9:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0aae692a108190ad7156d9b3c72901 completed May 18, 2026, 6:15 a.m.
NEDg Description generation batch_6a0ab28bc764819083ba5f419d288eab completed May 18, 2026, 6:32 a.m.
NED2 Entity disambiguation (via description) batch_6a0ab32113748190bfd86bcffcf06b76 completed May 18, 2026, 6:35 a.m.
Created at: April 16, 2026, 8:37 p.m.