Triple

T13848587
Position Surface form Disambiguated ID Type / Status
Subject Sam Kerr E332872 entity
Predicate hasSibling P363 FINISHED
Object Daniel Kerr
Daniel Kerr is a former Australian rules footballer best known for his career with the West Coast Eagles in the AFL.
E1077803 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: Daniel Kerr | Statement: [Sam Kerr, hasSibling, Daniel Kerr]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daniel Kerr
Context triple: [Sam Kerr, hasSibling, Daniel Kerr]
  • A. Dominic Kinnear
    Dominic Kinnear is a Scottish-American soccer coach and former player best known for leading the Houston Dynamo to multiple MLS Cup titles in the mid-2000s.
  • B. Andrew Kelley
    Andrew Kelley is a software engineer best known as the creator and lead developer of the Zig programming language.
  • C. Jason Kingsley
    Jason Kingsley is a British entrepreneur and game developer best known as the co-founder and CEO of the video game studio Rebellion Developments.
  • D. Christopher Henderson
    Christopher Henderson is a fictional high-ranking counterterrorism operative and former mentor to Jack Bauer in the television series "24."
  • E. Leo Parker
    Leo Parker was an American baritone saxophonist known for his work in the bebop and hard bop jazz scenes of the 1940s and 1950s.
  • 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: Daniel Kerr
Triple: [Sam Kerr, hasSibling, Daniel Kerr]
Generated description
Daniel Kerr is a former Australian rules footballer best known for his career with the West Coast Eagles in the AFL.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Daniel Kerr
Target entity description: Daniel Kerr is a former Australian rules footballer best known for his career with the West Coast Eagles in the AFL.
  • A. Dominic Kinnear
    Dominic Kinnear is a Scottish-American soccer coach and former player best known for leading the Houston Dynamo to multiple MLS Cup titles in the mid-2000s.
  • B. Andrew Kelley
    Andrew Kelley is a software engineer best known as the creator and lead developer of the Zig programming language.
  • C. Jason Kingsley
    Jason Kingsley is a British entrepreneur and game developer best known as the co-founder and CEO of the video game studio Rebellion Developments.
  • D. Christopher Henderson
    Christopher Henderson is a fictional high-ranking counterterrorism operative and former mentor to Jack Bauer in the television series "24."
  • E. Leo Parker
    Leo Parker was an American baritone saxophonist known for his work in the bebop and hard bop jazz scenes of the 1940s and 1950s.
  • 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_69d81c5ba13c8190839315f54768acfd completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de02b2a9788190b164760adec64ef6 completed April 14, 2026, 9:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69fcb648146c8190842a3da4e4c0e217 completed May 7, 2026, 3:56 p.m.
NEDg Description generation batch_69fcc51bf140819097bb29bbaf766dcc completed May 7, 2026, 5 p.m.
NED2 Entity disambiguation (via description) batch_69fcc61353d481908192a1e2f44e6a94 completed May 7, 2026, 5:04 p.m.
Created at: April 9, 2026, 10:14 p.m.