Triple

T14164476
Position Surface form Disambiguated ID Type / Status
Subject Ivan Vanko E351034 entity
Predicate familyName P18 FINISHED
Object Vanko
Vanko is a Slavic surname most notably associated with the Marvel character Ivan Vanko, the villain Whiplash from the film "Iron Man 2."
 E1083230 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: Vanko | Statement: [Ivan Vanko, familyName, Vanko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vanko
Context triple: [Ivan Vanko, familyName, Vanko]
  • A. Vinovo
    Vinovo is a municipality in Italy’s Piedmont region, located near Turin and known for hosting Juventus’ training facilities and women’s team matches.
  • B. Vianor
    Vianor is a tire and car service retail chain owned by Nokian Tyres, operating service centers and shops in multiple countries.
  • C. Vaskina
    Vaskina is a small settlement located in the traditional Tsakonian region of the eastern Peloponnese in Greece.
  • D. Vassy
    Vassy is a commune in northeastern France historically known as the site of the 1562 Massacre of Vassy, an event that helped ignite the French Wars of Religion.
  • E. Vinka
    Vinka is a Finnish military training aircraft developed by Valmet for basic pilot instruction.
  • 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: Vanko
Triple: [Ivan Vanko, familyName, Vanko]
Generated description
Vanko is a Slavic surname most notably associated with the Marvel character Ivan Vanko, the villain Whiplash from the film "Iron Man 2."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vanko
Target entity description: Vanko is a Slavic surname most notably associated with the Marvel character Ivan Vanko, the villain Whiplash from the film "Iron Man 2."
  • A. Vinovo
    Vinovo is a municipality in Italy’s Piedmont region, located near Turin and known for hosting Juventus’ training facilities and women’s team matches.
  • B. Vianor
    Vianor is a tire and car service retail chain owned by Nokian Tyres, operating service centers and shops in multiple countries.
  • C. Vaskina
    Vaskina is a small settlement located in the traditional Tsakonian region of the eastern Peloponnese in Greece.
  • D. Vassy
    Vassy is a commune in northeastern France historically known as the site of the 1562 Massacre of Vassy, an event that helped ignite the French Wars of Religion.
  • E. Vinka
    Vinka is a Finnish military training aircraft developed by Valmet for basic pilot instruction.
  • 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_69d8278775fc8190b0802d22ca2f495d completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de61b207cc8190b85b1ff0910b54da completed April 14, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcf7f3170481909f3981c1e56235d9 completed May 7, 2026, 8:37 p.m.
NEDg Description generation batch_69fcfe9debe08190b9943f941f1b8813 completed May 7, 2026, 9:05 p.m.
NED2 Entity disambiguation (via description) batch_69fcff29f7608190b4b7d24fc1e011bf completed May 7, 2026, 9:07 p.m.
Created at: April 10, 2026, 12:59 a.m.