Triple

T18518871
Position Surface form Disambiguated ID Type / Status
Subject An Actor Prepares E452533 entity
Predicate mainCharacter P1183 FINISHED
Object Tortsov
Tortsov is the fictional theatre director and acting teacher who guides students through Konstantin Stanislavski’s system in the book *An Actor Prepares*.
 E1329140 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: Tortsov | Statement: [An Actor Prepares, mainCharacter, Tortsov]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tortsov
Context triple: [An Actor Prepares, mainCharacter, Tortsov]
  • A. Yuryatin
    Yuryatin is a fictional Russian town in Boris Pasternak’s novel "Doctor Zhivago," serving as a key setting in Lara Antipova’s story.
  • B. Khovrino
    Khovrino is a Moscow Metro station serving as the northern terminus of the Zamoskvoretskaya Line.
  • C. Vyazemsky
    Vyazemsky is a small town in Russia’s Far Eastern Federal District, serving as an administrative center within Khabarovsk Krai.
  • D. Tarasova
    Tarasova is a Russian surname most prominently associated with Tatiana Tarasova, a renowned figure skating coach and former competitor.
  • E. Grusinskaya
    Grusinskaya is a fading but still celebrated Russian ballerina whose loneliness and vulnerability are central to the drama of the film "Grand Hotel."
  • 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: Tortsov
Triple: [An Actor Prepares, mainCharacter, Tortsov]
Generated description
Tortsov is the fictional theatre director and acting teacher who guides students through Konstantin Stanislavski’s system in the book *An Actor Prepares*.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tortsov
Target entity description: Tortsov is the fictional theatre director and acting teacher who guides students through Konstantin Stanislavski’s system in the book *An Actor Prepares*.
  • A. Yuryatin
    Yuryatin is a fictional Russian town in Boris Pasternak’s novel "Doctor Zhivago," serving as a key setting in Lara Antipova’s story.
  • B. Khovrino
    Khovrino is a Moscow Metro station serving as the northern terminus of the Zamoskvoretskaya Line.
  • C. Vyazemsky
    Vyazemsky is a small town in Russia’s Far Eastern Federal District, serving as an administrative center within Khabarovsk Krai.
  • D. Tarasova
    Tarasova is a Russian surname most prominently associated with Tatiana Tarasova, a renowned figure skating coach and former competitor.
  • E. Grusinskaya
    Grusinskaya is a fading but still celebrated Russian ballerina whose loneliness and vulnerability are central to the drama of the film "Grand Hotel."
  • 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_69d8d386df84819092355ebb260d848e completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5338be20c8190bc7fe8de050345a2 completed April 19, 2026, 7:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a04916eeed88190bf8df75fc83ba145 completed May 13, 2026, 2:57 p.m.
NEDg Description generation batch_6a04924c2fd08190a824c470b732626f completed May 13, 2026, 3:01 p.m.
NED2 Entity disambiguation (via description) batch_6a0492e3a6dc8190902bb46bec64c07e completed May 13, 2026, 3:04 p.m.
Created at: April 10, 2026, 11:36 a.m.