Triple

T16330428
Position Surface form Disambiguated ID Type / Status
Subject Alexander Ridha E396535 entity
Predicate hasCollaborationWith P398 FINISHED
Object Tiga
Tiga is a Canadian DJ and electronic music producer known for his influential work in techno and electro house.
E1206588 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: Tiga | Statement: [Alexander Ridha, hasCollaborationWith, Tiga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tiga
Context triple: [Alexander Ridha, hasCollaborationWith, Tiga]
  • A. Tretes
    Tretes is a cool highland resort town in East Java, Indonesia, known for its mountain scenery, villas, and as a gateway to nearby peaks like Mount Arjuno.
  • B. Tanto
    Tanto was a former town in Hyōgo Prefecture, Japan, that later became part of the expanded city of Toyooka through municipal merger.
  • C. Trois
    Trois is an erotic thriller film directed by Rob Hardy that gained attention in the early 2000s for its independent production and success within the urban cinema market.
  • D. Kogo
    Kogo is a settlement located in the Litoral region of Equatorial Guinea.
  • E. Tré
    Tré is the stage name of Frank Edwin Wright III, the energetic and long-time drummer of the American punk rock band Green Day.
  • 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: Tiga
Triple: [Alexander Ridha, hasCollaborationWith, Tiga]
Generated description
Tiga is a Canadian DJ and electronic music producer known for his influential work in techno and electro house.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tiga
Target entity description: Tiga is a Canadian DJ and electronic music producer known for his influential work in techno and electro house.
  • A. Tretes
    Tretes is a cool highland resort town in East Java, Indonesia, known for its mountain scenery, villas, and as a gateway to nearby peaks like Mount Arjuno.
  • B. Tanto
    Tanto was a former town in Hyōgo Prefecture, Japan, that later became part of the expanded city of Toyooka through municipal merger.
  • C. Trois
    Trois is an erotic thriller film directed by Rob Hardy that gained attention in the early 2000s for its independent production and success within the urban cinema market.
  • D. Kogo
    Kogo is a settlement located in the Litoral region of Equatorial Guinea.
  • E. Tré
    Tré is the stage name of Frank Edwin Wright III, the energetic and long-time drummer of the American punk rock band Green Day.
  • 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_69d87f255b788190a400eba031dd85d8 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2c4debef08190a64f13214bfa098f completed April 17, 2026, 11:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00261134108190812da262b424a476 completed May 10, 2026, 6:30 a.m.
NEDg Description generation batch_6a0026c9b5c481908f60d2ebfb3f71d7 completed May 10, 2026, 6:33 a.m.
NED2 Entity disambiguation (via description) batch_6a00273dd8fc8190b84b8a96442781ee completed May 10, 2026, 6:35 a.m.
Created at: April 10, 2026, 5:07 a.m.