Triple

T5192243
Position Surface form Disambiguated ID Type / Status
Subject Gabriel Byrne E117182 entity
Predicate notableWork P4 FINISHED
Object The 33
The 33 is a 2015 drama film that recounts the true story of the 2010 Chilean mining disaster and the rescue of 33 trapped miners.
 E502217 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 33 | Statement: [Gabriel Byrne, notableWork, The 33]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: The 33
Context triple: [Gabriel Byrne, notableWork, The 33]
  • A. The 305
    The 305 is a nickname commonly used to refer to Miami, Florida, derived from its original area code.
  • B. 13 Going on 30
    13 Going on 30 is a 2004 romantic comedy fantasy film about a 13-year-old girl who magically wakes up in her 30-year-old body and must navigate adulthood, starring Jennifer Garner.
  • C. Time for Three
    Time for Three is a genre-blending string trio known for fusing classical music with jazz, pop, and other contemporary styles in highly energetic performances.
  • D. Thirteen
    Thirteen is a studio album by American country and folk singer-songwriter Emmylou Harris.
  • E. Thirteen
    Thirteen is a 2003 coming-of-age drama film co-written by and starring Nikki Reed that explores the turbulent adolescence of a thirteen-year-old girl.
  • 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 33
Triple: [Gabriel Byrne, notableWork, The 33]
Generated description
The 33 is a 2015 drama film that recounts the true story of the 2010 Chilean mining disaster and the rescue of 33 trapped miners.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: The 33
Target entity description: The 33 is a 2015 drama film that recounts the true story of the 2010 Chilean mining disaster and the rescue of 33 trapped miners.
  • A. The 305
    The 305 is a nickname commonly used to refer to Miami, Florida, derived from its original area code.
  • B. 13 Going on 30
    13 Going on 30 is a 2004 romantic comedy fantasy film about a 13-year-old girl who magically wakes up in her 30-year-old body and must navigate adulthood, starring Jennifer Garner.
  • C. Time for Three
    Time for Three is a genre-blending string trio known for fusing classical music with jazz, pop, and other contemporary styles in highly energetic performances.
  • D. Thirteen
    Thirteen is a studio album by American country and folk singer-songwriter Emmylou Harris.
  • E. Thirteen
    Thirteen is a 2003 coming-of-age drama film co-written by and starring Nikki Reed that explores the turbulent adolescence of a thirteen-year-old girl.
  • 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_69bd4462ed04819084fcb01eb9d2fa74 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd79efd16c8190b0b16278a00baecd completed March 20, 2026, 4:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69bee0943ce48190838aff0cfd12c655 completed March 21, 2026, 6:16 p.m.
NEDg Description generation batch_69bee5fc0c408190b4ad4b77e0045182 completed March 21, 2026, 6:39 p.m.
NED2 Entity disambiguation (via description) batch_69bee6b954c08190a353ebcfe829888a completed March 21, 2026, 6:43 p.m.
Created at: March 20, 2026, 1:46 p.m.