Triple

T7775640
Position Surface form Disambiguated ID Type / Status
Subject Scarlet Street E221382 entity
Predicate starring P1507 FINISHED
Object Jess Barker
Jess Barker was an American film and television actor active in the mid-20th century, known for his roles in crime dramas and noir films.
 E693021 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: Jess Barker | Statement: [Scarlet Street, starring, Jess Barker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jess Barker
Context triple: [Scarlet Street, starring, Jess Barker]
  • A. Jeremy Black
    Jeremy Black is a British historian renowned for his prolific scholarship on military history, international relations, and the history of warfare.
  • B. Kim Barker
    Kim Barker is an American screenwriter best known for writing the romantic comedy film "License to Wed."
  • C. Graham Carr
    Graham Carr is a Canadian academic and administrator who serves as the president of Concordia University in Montreal.
  • D. C. K. Robinson
    C. K. Robinson was a 19th-century British architect best known for designing St. Paul’s Cathedral in Kolkata, a prominent example of Indo-Gothic architecture in India.
  • E. Ben Aaronovitch
    Ben Aaronovitch is a British author and screenwriter best known for his urban fantasy "Rivers of London" series, which blends police procedural elements with magic in a contemporary London setting.
  • 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: Jess Barker
Triple: [Scarlet Street, starring, Jess Barker]
Generated description
Jess Barker was an American film and television actor active in the mid-20th century, known for his roles in crime dramas and noir films.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jess Barker
Target entity description: Jess Barker was an American film and television actor active in the mid-20th century, known for his roles in crime dramas and noir films.
  • A. Jeremy Black
    Jeremy Black is a British historian renowned for his prolific scholarship on military history, international relations, and the history of warfare.
  • B. Kim Barker
    Kim Barker is an American screenwriter best known for writing the romantic comedy film "License to Wed."
  • C. Graham Carr
    Graham Carr is a Canadian academic and administrator who serves as the president of Concordia University in Montreal.
  • D. C. K. Robinson
    C. K. Robinson was a 19th-century British architect best known for designing St. Paul’s Cathedral in Kolkata, a prominent example of Indo-Gothic architecture in India.
  • E. Ben Aaronovitch
    Ben Aaronovitch is a British author and screenwriter best known for his urban fantasy "Rivers of London" series, which blends police procedural elements with magic in a contemporary London setting.
  • 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_69ca83ebbef881909ac47f789145fef7 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69caa4d005808190ac14c8d716421bdb completed March 30, 2026, 4:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69caf58a86548190b870417692e4b654 completed March 30, 2026, 10:13 p.m.
NEDg Description generation batch_69caf81d934881908fa41ebd43f3b2e2 completed March 30, 2026, 10:24 p.m.
NED2 Entity disambiguation (via description) batch_69caf9f86d808190880f7bb2fc8d4fe3 completed March 30, 2026, 10:32 p.m.
Created at: March 30, 2026, 3:46 p.m.