Triple

T12435586
Position Surface form Disambiguated ID Type / Status
Subject Jay Garner E297134 entity
Predicate employer P7 FINISHED
Object SY Coleman
SY Coleman is a company that employed American actor and director Jay Garner.
 E980663 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: SY Coleman | Statement: [Jay Garner, employer, SY Coleman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SY Coleman
Context triple: [Jay Garner, employer, SY Coleman]
  • A. Cy Coleman
    Cy Coleman was an American composer and songwriter best known for his work on Broadway musicals such as "Sweet Charity," "Barnum," and "City of Angels."
  • B. Kevin Coleman
    Kevin Coleman is an American local politician serving as the mayor of Westland, Michigan.
  • C. Vincent Coleman
    Vincent Coleman was a Canadian railway dispatcher remembered as a hero for staying at his post to warn an incoming train before being killed in the 1917 Halifax Explosion.
  • D. Jeff Cole
    Jeff Cole is the undercover police officer protagonist in the crime thriller film "In Too Deep," who infiltrates a powerful drug syndicate.
  • E. Derrick Coleman
    Derrick Coleman is a former NBA All-Star power forward and center best known for his dominant collegiate career at Syracuse University and being selected first overall in the 1990 NBA Draft.
  • 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: SY Coleman
Triple: [Jay Garner, employer, SY Coleman]
Generated description
SY Coleman is a company that employed American actor and director Jay Garner.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SY Coleman
Target entity description: SY Coleman is a company that employed American actor and director Jay Garner.
  • A. Cy Coleman
    Cy Coleman was an American composer and songwriter best known for his work on Broadway musicals such as "Sweet Charity," "Barnum," and "City of Angels."
  • B. Kevin Coleman
    Kevin Coleman is an American local politician serving as the mayor of Westland, Michigan.
  • C. Vincent Coleman
    Vincent Coleman was a Canadian railway dispatcher remembered as a hero for staying at his post to warn an incoming train before being killed in the 1917 Halifax Explosion.
  • D. Jeff Cole
    Jeff Cole is the undercover police officer protagonist in the crime thriller film "In Too Deep," who infiltrates a powerful drug syndicate.
  • E. Derrick Coleman
    Derrick Coleman is a former NBA All-Star power forward and center best known for his dominant collegiate career at Syracuse University and being selected first overall in the 1990 NBA Draft.
  • 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_69d6ada0640c81908c061d7fb3d47786 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d8c8fd481909b35ac504127a1b6 completed April 10, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6349f0f34819080e7d7f83f7baece completed May 2, 2026, 5:30 p.m.
NEDg Description generation batch_69f6359a52b4819096c3f520a5714b0a completed May 2, 2026, 5:34 p.m.
NED2 Entity disambiguation (via description) batch_69f63693f5c881909a9683a0c6a68739 completed May 2, 2026, 5:38 p.m.
Created at: April 8, 2026, 9:55 p.m.