Triple

T20350189
Position Surface form Disambiguated ID Type / Status
Subject Street Fighter II E495984 entity
Predicate bossCharacter P98692 FINISHED
Object Vega
Vega is a masked, claw-wielding Spanish ninja and acrobatic antagonist from the Street Fighter fighting game series.
 E1425742 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: Vega | Statement: [Street Fighter II, bossCharacter, Vega]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vega
Context triple: [Street Fighter II, bossCharacter, Vega]
  • A. Vega
    Vega is a Norwegian island renowned for its UNESCO-listed archipelago, traditional eiderdown harvesting, and rich coastal birdlife.
  • B. Vega
    Vega is a European small-lift launch vehicle developed by the European Space Agency and partners, primarily used to place light payloads into low Earth orbit.
  • C. Vega
    Vega is a common Spanish surname borne by numerous notable individuals across fields such as entertainment, sports, and politics.
  • D. Vega
    Vega is a residential locality in Haninge Municipality, Stockholm County, Sweden, known for its commuter rail station and growing suburban housing developments.
  • E. Vega
    Vega is an open-source visualization grammar and toolkit for creating, sharing, and exploring interactive data visualizations in a declarative JSON format.
  • 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: Vega
Triple: [Street Fighter II, bossCharacter, Vega]
Generated description
Vega is a masked, claw-wielding Spanish ninja and acrobatic antagonist from the Street Fighter fighting game series.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vega
Target entity description: Vega is a masked, claw-wielding Spanish ninja and acrobatic antagonist from the Street Fighter fighting game series.
  • A. Vega
    Vega is a European small-lift launch vehicle developed by the European Space Agency and partners, primarily used to place light payloads into low Earth orbit.
  • B. Vega
    Vega is a common Spanish surname borne by numerous notable individuals across fields such as entertainment, sports, and politics.
  • C. Vega
    Vega is a residential locality in Haninge Municipality, Stockholm County, Sweden, known for its commuter rail station and growing suburban housing developments.
  • D. Vega
    Vega is a Norwegian island renowned for its UNESCO-listed archipelago, traditional eiderdown harvesting, and rich coastal birdlife.
  • E. Vega
    Vega is an open-source visualization grammar and toolkit for creating, sharing, and exploring interactive data visualizations in a declarative JSON format.
  • 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_69e0b4a3320881909495ae8bc30bc2dc completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6784eacf4819095504e541d1d284d completed April 20, 2026, 7:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0869656ae4819084679d901249c2f7 completed May 16, 2026, 12:56 p.m.
NEDg Description generation batch_6a0869cc7308819087028f44af0386c2 completed May 16, 2026, 12:57 p.m.
NED2 Entity disambiguation (via description) batch_6a086ab4c0a08190a1cd3ca5ea07512f completed May 16, 2026, 1:01 p.m.
Created at: April 16, 2026, 11:24 a.m.