Triple

T14104654
Position Surface form Disambiguated ID Type / Status
Subject Two Trains Running E339473 entity
Predicate hasCharacter P2308 FINISHED
Object Risa
Risa is a character in August Wilson's play "Two Trains Running," known as a young, resilient waitress whose personal struggles and aspirations reflect the broader social and emotional tensions of the story.
 E1080910 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: Risa | Statement: [Two Trains Running, hasCharacter, Risa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Risa
Context triple: [Two Trains Running, hasCharacter, Risa]
  • A. Takako
    Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
  • B. Rina
    Rina is a feminine given name commonly used as a short or diminutive form of longer names such as Caterina.
  • C. Marisa
    Marisa is a feminine given name of Latin origin, commonly used in Spanish- and Italian-speaking cultures.
  • D. Keiko
    Keiko was a famous captive orca best known for starring in the film "Free Willy" and later becoming the focus of a high-profile rehabilitation and release effort.
  • E. Yoriko
    Yoriko is a Japanese feminine given name commonly borne by women and girls in Japan.
  • 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: Risa
Triple: [Two Trains Running, hasCharacter, Risa]
Generated description
Risa is a character in August Wilson's play "Two Trains Running," known as a young, resilient waitress whose personal struggles and aspirations reflect the broader social and emotional tensions of the story.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Risa
Target entity description: Risa is a character in August Wilson's play "Two Trains Running," known as a young, resilient waitress whose personal struggles and aspirations reflect the broader social and emotional tensions of the story.
  • A. Takako
    Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
  • B. Rina
    Rina is a feminine given name commonly used as a short or diminutive form of longer names such as Caterina.
  • C. Marisa
    Marisa is a feminine given name of Latin origin, commonly used in Spanish- and Italian-speaking cultures.
  • D. Keiko
    Keiko was a famous captive orca best known for starring in the film "Free Willy" and later becoming the focus of a high-profile rehabilitation and release effort.
  • E. Yoriko
    Yoriko is a Japanese feminine given name commonly borne by women and girls in Japan.
  • 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_69d81c69b5c8819094aa1abf18302908 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5fbd02888190bf07fd6d8769b61c completed April 14, 2026, 3:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcd0b48e448190b4fb8cb33e5d97e6 completed May 7, 2026, 5:49 p.m.
NEDg Description generation batch_69fcd288bd5881908f6a442201c5beea completed May 7, 2026, 5:57 p.m.
NED2 Entity disambiguation (via description) batch_69fcd3ad7be8819094fc71c9f44fb4cb completed May 7, 2026, 6:02 p.m.
Created at: April 9, 2026, 10:22 p.m.