Triple

T9217940
Position Surface form Disambiguated ID Type / Status
Subject Kristanna Loken E221286 entity
Predicate spouse P13 FINISHED
Object Noah Danby
Noah Danby is a Canadian actor known for his work in science fiction and action television series and films.
 E786011 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: Noah Danby | Statement: [Kristanna Loken, spouse, Noah Danby]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Noah Danby
Context triple: [Kristanna Loken, spouse, Noah Danby]
  • A. Noah Hanifin
    Noah Hanifin is an American professional ice hockey defenseman who has played in the NHL after starring as a top collegiate player at Boston College.
  • B. Noah Segan
    Noah Segan is an American actor best known for his frequent collaborations with director Rian Johnson, including roles in films like "Looper" and "Knives Out."
  • C. Noah Taylor
    Noah Taylor is an Australian actor known for his character roles in films such as "Shine," "Almost Famous," and "Game of Thrones."
  • D. Noah Young
    Noah Young was an American silent film actor and comedian best known for his supporting roles alongside Harold Lloyd in early 20th-century slapstick comedies.
  • E. Noah Mills
    Noah Mills is a Canadian model and actor known for his work in high-fashion campaigns and for prominent television roles, including in crime and drama series.
  • 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: Noah Danby
Triple: [Kristanna Loken, spouse, Noah Danby]
Generated description
Noah Danby is a Canadian actor known for his work in science fiction and action television series and films.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Noah Danby
Target entity description: Noah Danby is a Canadian actor known for his work in science fiction and action television series and films.
  • A. Noah Hanifin
    Noah Hanifin is an American professional ice hockey defenseman who has played in the NHL after starring as a top collegiate player at Boston College.
  • B. Noah Segan
    Noah Segan is an American actor best known for his frequent collaborations with director Rian Johnson, including roles in films like "Looper" and "Knives Out."
  • C. Noah Taylor
    Noah Taylor is an Australian actor known for his character roles in films such as "Shine," "Almost Famous," and "Game of Thrones."
  • D. Noah Young
    Noah Young was an American silent film actor and comedian best known for his supporting roles alongside Harold Lloyd in early 20th-century slapstick comedies.
  • E. Noah Mills
    Noah Mills is a Canadian model and actor known for his work in high-fashion campaigns and for prominent television roles, including in crime and drama series.
  • 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_69ca83eae42c8190a0ea9e040710a277 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccda0ae3d081908ff3f5dab52df5ae completed April 1, 2026, 8:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0662427dc81908cb9bfacc5b9e0f5 completed April 4, 2026, 1:15 a.m.
NEDg Description generation batch_69d0697b506c8190b14acbec1266f4bc completed April 4, 2026, 1:29 a.m.
NED2 Entity disambiguation (via description) batch_69d06a01520081908f697cf285c88c00 completed April 4, 2026, 1:31 a.m.
Created at: March 30, 2026, 7:27 p.m.