Triple

T14642072
Position Surface form Disambiguated ID Type / Status
Subject Back to School E343748 entity
Predicate screenwriter P2831 FINISHED
Object Will Porter
Will Porter is a screenwriter best known for his work on the 1986 Rodney Dangerfield comedy film "Back to School."
 E1112541 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: Will Porter | Statement: [Back to School, screenwriter, Will Porter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Will Porter
Context triple: [Back to School, screenwriter, Will Porter]
  • A. Thomas Porter
    Thomas Porter is a prominent computer graphics researcher and pioneer known for his influential work in digital image compositing and contributions recognized by the Steven A. Coons Award.
  • B. John Porter
    John Porter is a British record producer and musician best known for his work on influential blues and rock albums.
  • C. Chris Porter
    Chris Porter is a music producer best known for his work on the hit song "Back for Good" by Take That.
  • D. Bob Porter
    Bob Porter is a central fictional character named in the work "Teachers," likely depicted as a key figure within an educational setting.
  • E. Carl Porter
    Carl Porter is a character from the television series "Revenge," known as the son of Amanda Clarke and Jack Porter.
  • 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: Will Porter
Triple: [Back to School, screenwriter, Will Porter]
Generated description
Will Porter is a screenwriter best known for his work on the 1986 Rodney Dangerfield comedy film "Back to School."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Will Porter
Target entity description: Will Porter is a screenwriter best known for his work on the 1986 Rodney Dangerfield comedy film "Back to School."
  • A. Thomas Porter
    Thomas Porter is a prominent computer graphics researcher and pioneer known for his influential work in digital image compositing and contributions recognized by the Steven A. Coons Award.
  • B. John Porter
    John Porter is a British record producer and musician best known for his work on influential blues and rock albums.
  • C. Chris Porter
    Chris Porter is a music producer best known for his work on the hit song "Back for Good" by Take That.
  • D. Bob Porter
    Bob Porter is a central fictional character named in the work "Teachers," likely depicted as a key figure within an educational setting.
  • E. Carl Porter
    Carl Porter is a character from the television series "Revenge," known as the son of Amanda Clarke and Jack Porter.
  • 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_69d822e1a2cc81908e5bb93cf61ce3cc completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb4e80aa48190884bab800f357106 completed April 14, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdd5d404e881908d26e684702ae122 completed May 8, 2026, 12:23 p.m.
NEDg Description generation batch_69fdd9dbdf448190ad40ba07f586b4f6 completed May 8, 2026, 12:41 p.m.
NED2 Entity disambiguation (via description) batch_69fdda3a7ea08190bc65641681da00cc completed May 8, 2026, 12:42 p.m.
Created at: April 10, 2026, 1:26 a.m.