Triple

T2665228
Position Surface form Disambiguated ID Type / Status
Subject Cedar Rapids E55618 entity
Predicate producer P490 FINISHED
Object Jim Burke
Jim Burke is an American film producer known for his work on acclaimed movies such as "The Descendants" and "Green Book."
 E310593 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: Jim Burke | Statement: [Cedar Rapids, producer, Jim Burke]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jim Burke
Context triple: [Cedar Rapids, producer, Jim Burke]
  • A. Phil Burke
    Phil Burke is a Canadian actor best known for his role as Mickey McGinnes on the television drama series "Hell on Wheels."
  • B. Ed McCauley
    Ed McCauley is a Canadian academic and research leader who serves as president of the University of Calgary.
  • C. Al Burton
    Al Burton was an American television producer and composer best known for his work on popular sitcoms such as "The Facts of Life."
  • D. Rob McKenna
    Rob McKenna is a perpetually rain-plagued lorry driver in Douglas Adams' "So Long, and Thanks for All the Fish," humorously revealed to be a Rain God unknowingly worshipped by clouds.
  • E. Rob McKenna
    Rob McKenna is an American attorney and politician best known for serving as the Attorney General of Washington State.
  • 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: Jim Burke
Triple: [Cedar Rapids, producer, Jim Burke]
Generated description
Jim Burke is an American film producer known for his work on acclaimed movies such as "The Descendants" and "Green Book."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jim Burke
Target entity description: Jim Burke is an American film producer known for his work on acclaimed movies such as "The Descendants" and "Green Book."
  • A. Phil Burke
    Phil Burke is a Canadian actor best known for his role as Mickey McGinnes on the television drama series "Hell on Wheels."
  • B. Ed McCauley
    Ed McCauley is a Canadian academic and research leader who serves as president of the University of Calgary.
  • C. Al Burton
    Al Burton was an American television producer and composer best known for his work on popular sitcoms such as "The Facts of Life."
  • D. Rob McKenna
    Rob McKenna is a perpetually rain-plagued lorry driver in Douglas Adams' "So Long, and Thanks for All the Fish," humorously revealed to be a Rain God unknowingly worshipped by clouds.
  • E. Rob McKenna
    Rob McKenna is an American attorney and politician best known for serving as the Attorney General of Washington State.
  • 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_69ab49e54de48190be708cd1cf8be073 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd96ed2748190a4feae98199b459d completed March 7, 2026, 7:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69b055bd41108190920b7397c16d15f5 completed March 10, 2026, 5:32 p.m.
NEDg Description generation batch_69b05d246a60819088510c8fa87402c2 completed March 10, 2026, 6:04 p.m.
NED2 Entity disambiguation (via description) batch_69b062a6241c81909f3217a4a33aa4c6 completed March 10, 2026, 6:27 p.m.
Created at: March 6, 2026, 9:54 p.m.