Triple

T5224679
Position Surface form Disambiguated ID Type / Status
Subject Hugh Marlowe E117955 entity
Predicate portrayed P1668 FINISHED
Object Tom Stevens
Tom Stevens is a fictional character played by actor Hugh Marlowe, best known from mid-20th-century American film and television.
 E503828 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: Tom Stevens | Statement: [Hugh Marlowe, portrayed, Tom Stevens]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tom Stevens
Context triple: [Hugh Marlowe, portrayed, Tom Stevens]
  • A. Don Stevens
    Don Stevens is a notable individual recognized for achievements significant enough to be distinguished from others sharing the surname Stevens.
  • B. David Stevens
    David Stevens was an Australian screenwriter and director best known for co-writing the acclaimed film "Breaker Morant" and his work in film, television, and theatre.
  • C. Mark Stevens
    Mark Stevens is a music producer known for his work with the artist Chaka.
  • D. Mark Stevens
    Mark Stevens was an American film and television actor best known for his roles in 1940s and 1950s dramas and film noir.
  • E. Roger Stevens
    Roger Stevens was a prominent British civil servant and diplomat who notably served as the first Vice-Chancellor of the University of Leeds.
  • 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: Tom Stevens
Triple: [Hugh Marlowe, portrayed, Tom Stevens]
Generated description
Tom Stevens is a fictional character played by actor Hugh Marlowe, best known from mid-20th-century American film and television.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tom Stevens
Target entity description: Tom Stevens is a fictional character played by actor Hugh Marlowe, best known from mid-20th-century American film and television.
  • A. Don Stevens
    Don Stevens is a notable individual recognized for achievements significant enough to be distinguished from others sharing the surname Stevens.
  • B. David Stevens
    David Stevens was an Australian screenwriter and director best known for co-writing the acclaimed film "Breaker Morant" and his work in film, television, and theatre.
  • C. Mark Stevens
    Mark Stevens is a music producer known for his work with the artist Chaka.
  • D. Mark Stevens
    Mark Stevens was an American film and television actor best known for his roles in 1940s and 1950s dramas and film noir.
  • E. Roger Stevens
    Roger Stevens was a prominent British civil servant and diplomat who notably served as the first Vice-Chancellor of the University of Leeds.
  • 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_69bd4465e03081909bfcfd7113062590 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7abd3ed48190bfd8d2f2ca399741 completed March 20, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69beeffc51888190938dc157b14c4b6c completed March 21, 2026, 7:22 p.m.
NEDg Description generation batch_69bef0b2b6448190be1c465738be741b completed March 21, 2026, 7:25 p.m.
NED2 Entity disambiguation (via description) batch_69bef121817c8190aebd27ee34c0a419 completed March 21, 2026, 7:27 p.m.
Created at: March 20, 2026, 1:48 p.m.