Triple

T16098324
Position Surface form Disambiguated ID Type / Status
Subject CHiPs E390544 entity
Predicate composer P1361 FINISHED
Object John Parker
John Parker is a film and television composer best known for scoring the popular 1970s–80s police drama series "CHiPs."
 E1194560 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: John Parker | Statement: [CHiPs, composer, John Parker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: John Parker
Context triple: [CHiPs, composer, John Parker]
  • A. John Parker
    John Parker was an American colonial militia captain best known for leading the Lexington militia at the opening skirmishes of the American Revolutionary War.
  • B. John Parker
    John Parker is an individual known primarily as the husband of Lydia Moore Parker.
  • C. John Parker
    John Parker was an American statesman who represented South Carolina as a delegate to the Continental Congress during the Revolutionary era.
  • D. Nathaniel Parker
    Nathaniel Parker is an English actor known for his work in film, television, and theatre, including roles in adaptations of classic literature and popular detective dramas.
  • E. Jonathan Jennings
    Jonathan Jennings was an American politician who became the first governor of the state of Indiana and played a key role in its transition from territory to statehood.
  • 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: John Parker
Triple: [CHiPs, composer, John Parker]
Generated description
John Parker is a film and television composer best known for scoring the popular 1970s–80s police drama series "CHiPs."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: John Parker
Target entity description: John Parker is a film and television composer best known for scoring the popular 1970s–80s police drama series "CHiPs."
  • A. John Parker
    John Parker was an American colonial militia captain best known for leading the Lexington militia at the opening skirmishes of the American Revolutionary War.
  • B. John Parker
    John Parker is an individual known primarily as the husband of Lydia Moore Parker.
  • C. John Parker
    John Parker was an American statesman who represented South Carolina as a delegate to the Continental Congress during the Revolutionary era.
  • D. Nathaniel Parker
    Nathaniel Parker is an English actor known for his work in film, television, and theatre, including roles in adaptations of classic literature and popular detective dramas.
  • E. Jonathan Jennings
    Jonathan Jennings was an American politician who became the first governor of the state of Indiana and played a key role in its transition from territory to statehood.
  • 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_69d87f198bc48190a8b7e53ca15b7ead completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e1ff6551a48190afb7e0c61e22b541 completed April 17, 2026, 9:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffeb9b3e708190be822f7ed588c9da completed May 10, 2026, 2:21 a.m.
NEDg Description generation batch_69ffec7d0b188190805a471ed3a97eb5 completed May 10, 2026, 2:25 a.m.
NED2 Entity disambiguation (via description) batch_69ffed254d8c81909e0d86621c7792cb completed May 10, 2026, 2:27 a.m.
Created at: April 10, 2026, 4:59 a.m.