Triple

T21481062
Position Surface form Disambiguated ID Type / Status
Subject Phillip Van Dyke E529992 entity
Predicate appearedIn P795 FINISHED
Object The Crew
The Crew is a 1990s American television series best known for its comedic portrayal of the lives and relationships of airline flight attendants.
 E1487197 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: The Crew | Statement: [Phillip Van Dyke, appearedIn, The Crew]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: The Crew
Context triple: [Phillip Van Dyke, appearedIn, The Crew]
  • A. The Crew
    The Crew is the popular nickname for the Columbus Crew, a Major League Soccer club based in Columbus, Ohio.
  • B. The Crew
    The Crew is a Marvel Comics superhero team, often featuring Black heroes like Black Panther and allies who confront social injustice and street-level threats.
  • C. The Crew
    The Crew is a 2000 American crime-comedy film about a group of retired mobsters who get pulled back into underworld antics in Miami.
  • D. The Crew
    The Crew is the passionate student cheering section that supports the University of Maryland Terrapins football team at home games.
  • E. The Crew franchise
    The Crew franchise is an open-world racing video game series known for its large-scale, persistent online environments and coast-to-coast driving across detailed recreations of real-world locations.
  • 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: The Crew
Triple: [Phillip Van Dyke, appearedIn, The Crew]
Generated description
The Crew is a 1990s American television series best known for its comedic portrayal of the lives and relationships of airline flight attendants.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: The Crew
Target entity description: The Crew is a 1990s American television series best known for its comedic portrayal of the lives and relationships of airline flight attendants.
  • A. The Crew
    The Crew is the popular nickname for the Columbus Crew, a Major League Soccer club based in Columbus, Ohio.
  • B. The Crew
    The Crew is a Marvel Comics superhero team, often featuring Black heroes like Black Panther and allies who confront social injustice and street-level threats.
  • C. The Crew
    The Crew is a 2000 American crime-comedy film about a group of retired mobsters who get pulled back into underworld antics in Miami.
  • D. The Crew
    The Crew is the passionate student cheering section that supports the University of Maryland Terrapins football team at home games.
  • E. The Crew franchise
    The Crew franchise is an open-world racing video game series known for its large-scale, persistent online environments and coast-to-coast driving across detailed recreations of real-world locations.
  • 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_69e0c45acc3881908e38d3f28964152b completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69e9ea338f988190a3044f8d02a567fe completed April 23, 2026, 9:45 a.m.
NED1 Entity disambiguation (via context triple) batch_6a09e131956081909633fccfffc97cc9 completed May 17, 2026, 3:39 p.m.
NEDg Description generation batch_6a09e1e8a698819093ed056a1a007aba completed May 17, 2026, 3:42 p.m.
NED2 Entity disambiguation (via description) batch_6a09e278b5488190a71119d3c62f64c0 completed May 17, 2026, 3:44 p.m.
Created at: April 16, 2026, 6:21 p.m.