Triple

T3911874
Position Surface form Disambiguated ID Type / Status
Subject Spaceways E87339 entity
Predicate hasCastMember P2308 FINISHED
Object Eva Bartok
Eva Bartok was a Hungarian-born British film actress known for her roles in 1950s and 1960s European and British cinema, including thrillers and adventure films.
E398432 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: Eva Bartok | Statement: [Spaceways, hasCastMember, Eva Bartok]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eva Bartok
Context triple: [Spaceways, hasCastMember, Eva Bartok]
  • A. Márta Borbíró
    Márta Borbíró was the wife of Hungarian physiologist and Nobel laureate Albert Szent-Györgyi.
  • B. Ilona Komocsin
    Ilona Komocsin was the wife and muse of Hungarian architect and sculptor Jenő Bory, for whom he built the romantic Bory Castle as a monument to their love.
  • C. Márta Károlyi
    Márta Károlyi is a Romanian-American gymnastics coach renowned for co-developing the dominant Romanian and later U.S. women's gymnastics programs alongside her husband, Béla Károlyi.
  • D. Ilona Kovács
    Ilona Kovács was the wife of renowned Hungarian-American film director Michael Curtiz.
  • E. Ruzena Bajcsy
    Ruzena Bajcsy is a pioneering computer scientist and engineer known for her influential work in robotics, computer vision, and artificial intelligence.
  • 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: Eva Bartok
Triple: [Spaceways, hasCastMember, Eva Bartok]
Generated description
Eva Bartok was a Hungarian-born British film actress known for her roles in 1950s and 1960s European and British cinema, including thrillers and adventure films.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Eva Bartok
Target entity description: Eva Bartok was a Hungarian-born British film actress known for her roles in 1950s and 1960s European and British cinema, including thrillers and adventure films.
  • A. Márta Borbíró
    Márta Borbíró was the wife of Hungarian physiologist and Nobel laureate Albert Szent-Györgyi.
  • B. Ilona Komocsin
    Ilona Komocsin was the wife and muse of Hungarian architect and sculptor Jenő Bory, for whom he built the romantic Bory Castle as a monument to their love.
  • C. Márta Károlyi
    Márta Károlyi is a Romanian-American gymnastics coach renowned for co-developing the dominant Romanian and later U.S. women's gymnastics programs alongside her husband, Béla Károlyi.
  • D. Ilona Kovács
    Ilona Kovács was the wife of renowned Hungarian-American film director Michael Curtiz.
  • E. Ruzena Bajcsy
    Ruzena Bajcsy is a pioneering computer scientist and engineer known for her influential work in robotics, computer vision, and artificial intelligence.
  • 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_69aed9424514819086e9c58adde6652d completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeed35e2d081908b5d87c7630e7ffc completed March 9, 2026, 3:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51cb454c48190bf47d080f6cc24f0 completed March 14, 2026, 8:30 a.m.
NEDg Description generation batch_69b5206dfd848190ae7aaa9997150934 completed March 14, 2026, 8:46 a.m.
NED2 Entity disambiguation (via description) batch_69b520ce6af481909b7824c2ec221331 completed March 14, 2026, 8:48 a.m.
Created at: March 9, 2026, 3:22 p.m.