Triple

T2425500
Position Surface form Disambiguated ID Type / Status
Subject Cane E53515 entity
Predicate containsCharacter P5716 FINISHED
Object Dan Moore
Dan Moore is a fictional character appearing in the work "Cane."
 E265859 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: Dan Moore | Statement: [Cane, containsCharacter, Dan Moore]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dan Moore
Context triple: [Cane, containsCharacter, Dan Moore]
  • A. Ben Moore
    Ben Moore is a British composer and musician recognized for his contributions to contemporary classical and choral music.
  • B. Ray Boone
    Ray Boone was an American Major League Baseball infielder and two-time All-Star who played primarily in the 1940s and 1950s and is part of a three-generation MLB family.
  • C. Sam O'Steen
    Sam O'Steen was an acclaimed American film editor best known for his work on influential films such as "The Graduate," "Chinatown," and "Cool Hand Luke."
  • D. Gerry Davis
    Gerry Davis was a British television writer and script editor best known for his work on Doctor Who, including co-creating the iconic Cybermen.
  • E. Gerry Davis
    Gerry Davis is a longtime Major League Baseball umpire known for working numerous postseason games and serving as a crew chief in multiple World 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: Dan Moore
Triple: [Cane, containsCharacter, Dan Moore]
Generated description
Dan Moore is a fictional character appearing in the work "Cane."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dan Moore
Target entity description: Dan Moore is a fictional character appearing in the work "Cane."
  • A. Ben Moore
    Ben Moore is a British composer and musician recognized for his contributions to contemporary classical and choral music.
  • B. Ray Boone
    Ray Boone was an American Major League Baseball infielder and two-time All-Star who played primarily in the 1940s and 1950s and is part of a three-generation MLB family.
  • C. Sam O'Steen
    Sam O'Steen was an acclaimed American film editor best known for his work on influential films such as "The Graduate," "Chinatown," and "Cool Hand Luke."
  • D. Gerry Davis
    Gerry Davis was a British television writer and script editor best known for his work on Doctor Who, including co-creating the iconic Cybermen.
  • E. Gerry Davis
    Gerry Davis is a longtime Major League Baseball umpire known for working numerous postseason games and serving as a crew chief in multiple World 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_69ab495c44d48190b7235b23719bc3f6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc99a773c819092d5f3c297b83887 completed March 7, 2026, 6:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69aebf61088481909d79e822e4071456 completed March 9, 2026, 12:38 p.m.
NEDg Description generation batch_69aec2e4fee481909704d329ad92f4ad completed March 9, 2026, 12:53 p.m.
NED2 Entity disambiguation (via description) batch_69aec39bb6a4819084652814e18f60d4 completed March 9, 2026, 12:56 p.m.
Created at: March 6, 2026, 9:42 p.m.