Triple

T12530471
Position Surface form Disambiguated ID Type / Status
Subject Matthew Lillard E299548 entity
Predicate notableWork P4 FINISHED
Object Good Girls
Good Girls is an American dark comedy-drama television series about three suburban mothers who turn to crime to solve their financial problems.
 E319400 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: Good Girls | Statement: [Matthew Lillard, notableWork, Good Girls]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Good Girls
Context triple: [Matthew Lillard, notableWork, Good Girls]
  • A. Good Girls
    Good Girls is an American dark comedy-drama television series about three suburban mothers who turn to crime to solve their financial problems.
  • B. Good Girls
    "Good Girls" is a pop-rock song by Australian band 5 Seconds of Summer, known for its catchy hooks and themes of defying good-girl stereotypes.
  • C. Very Good Girls
    Very Good Girls is a 2013 coming-of-age drama film about two best friends whose bond is tested when they fall for the same young man.
  • D. Good Girls, Bad Guys
    "Good Girls, Bad Guys" is a hip-hop track by DMX from his 1999 album "...And Then There Was X."
  • E. Goodtime Girls
    Goodtime Girls is an early-1980s American sitcom that followed the comedic misadventures of young women sharing an apartment during World War II.
  • 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: Good Girls
Triple: [Matthew Lillard, notableWork, Good Girls]
Generated description
Good Girls is an American dark comedy-drama television series about three suburban mothers who turn to crime to solve their financial problems.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Good Girls
Target entity description: Good Girls is an American dark comedy-drama television series about three suburban mothers who turn to crime to solve their financial problems.
  • A. Good Girls chosen
    Good Girls is an American dark comedy-drama television series about three suburban mothers who turn to crime to solve their financial problems.
  • B. Good Girls
    "Good Girls" is a pop-rock song by Australian band 5 Seconds of Summer, known for its catchy hooks and themes of defying good-girl stereotypes.
  • C. Very Good Girls
    Very Good Girls is a 2013 coming-of-age drama film about two best friends whose bond is tested when they fall for the same young man.
  • D. Good Girls, Bad Guys
    "Good Girls, Bad Guys" is a hip-hop track by DMX from his 1999 album "...And Then There Was X."
  • E. Goodtime Girls
    Goodtime Girls is an early-1980s American sitcom that followed the comedic misadventures of young women sharing an apartment during World War II.
  • F. None of above.

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_69d6ada5cdd48190860d9ce30aff69be completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d95469d100819087c83bc55e3ec9ce completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f64bc674e881908673e1f9103cc8be completed May 2, 2026, 7:08 p.m.
NEDg Description generation batch_69f64c7d337c81909ee9ea52158a756f completed May 2, 2026, 7:11 p.m.
NED2 Entity disambiguation (via description) batch_69f64d653b988190b11d061f55ef7192 completed May 2, 2026, 7:15 p.m.
Created at: April 8, 2026, 9:57 p.m.