Triple

T7355103
Position Surface form Disambiguated ID Type / Status
Subject Passenger 57 E169602 entity
Predicate producer P490 FINISHED
Object Dan Paulson
Dan Paulson is a film and television producer best known for his work on action films like "Passenger 57."
 E690104 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 Paulson | Statement: [Passenger 57, producer, Dan Paulson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dan Paulson
Context triple: [Passenger 57, producer, Dan Paulson]
  • A. Don Brautigam
    Don Brautigam was an American illustrator best known for his striking, realistic cover art for horror and thriller novels, including works by Stephen King.
  • B. Dean Paul Larson
    Dean Paul Larson is a fictional character from the television series "The Chair."
  • C. Ron Carlson
    Ron Carlson is an American author and educator known for his acclaimed short stories and novels that often explore everyday lives with humor and emotional depth.
  • D. Michael Larsen
    Michael Larsen is the person credited with coining the now-popular term “Painted Ladies” to describe the colorfully restored Victorian and Edwardian houses of San Francisco.
  • E. Tad Horvath
    Tad Horvath is a fictional character from the television series "Girls," known as the somewhat conservative and later openly gay father of the main character, Hannah Horvath.
  • 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 Paulson
Triple: [Passenger 57, producer, Dan Paulson]
Generated description
Dan Paulson is a film and television producer best known for his work on action films like "Passenger 57."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dan Paulson
Target entity description: Dan Paulson is a film and television producer best known for his work on action films like "Passenger 57."
  • A. Don Brautigam
    Don Brautigam was an American illustrator best known for his striking, realistic cover art for horror and thriller novels, including works by Stephen King.
  • B. Dean Paul Larson
    Dean Paul Larson is a fictional character from the television series "The Chair."
  • C. Ron Carlson
    Ron Carlson is an American author and educator known for his acclaimed short stories and novels that often explore everyday lives with humor and emotional depth.
  • D. Michael Larsen
    Michael Larsen is the person credited with coining the now-popular term “Painted Ladies” to describe the colorfully restored Victorian and Edwardian houses of San Francisco.
  • E. Tad Horvath
    Tad Horvath is a fictional character from the television series "Girls," known as the somewhat conservative and later openly gay father of the main character, Hannah Horvath.
  • 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_69c68a59f2288190877ca15c19b1e822 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f10e71fc81909307ca39a61142d3 completed March 27, 2026, 9:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69c91f49faec8190b4920097d52896f3 completed March 29, 2026, 12:47 p.m.
NEDg Description generation batch_69c91fc5a1048190b5d4c988efa99713 completed March 29, 2026, 12:49 p.m.
NED2 Entity disambiguation (via description) batch_69c92043ee988190a1ce0ee6090eb5fc completed March 29, 2026, 12:51 p.m.
Created at: March 27, 2026, 3:05 p.m.