Triple

T11088620
Position Surface form Disambiguated ID Type / Status
Subject Paul Laverty E262188 entity
Predicate wroteScreenplayFor P15305 FINISHED
Object Yuli
Yuli is a film for which screenwriter Paul Laverty wrote the screenplay, likely reflecting his characteristic focus on socially conscious, character-driven storytelling.
 E904109 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: Yuli | Statement: [Paul Laverty, wroteScreenplayFor, Yuli]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yuli
Context triple: [Paul Laverty, wroteScreenplayFor, Yuli]
  • A. Oksana
    Oksana is a feminine given name of Ukrainian origin, most famously borne by Olympic champion figure skater Oksana Baiul.
  • B. Yelena
    Yelena is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Helen or Helena in English.
  • C. Yulia
    Yulia is a feminine given name, commonly used in Slavic countries as a form of the name Julia.
  • D. Yulia Makhalina
    Yulia Makhalina is a renowned Russian ballerina celebrated as a principal dancer of the Mariinsky Ballet, noted for her elegant classical technique and dramatic stage presence.
  • E. Tatjana
    Tatjana is a feminine given name, commonly used in various European countries as a variant of Tatyana.
  • 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: Yuli
Triple: [Paul Laverty, wroteScreenplayFor, Yuli]
Generated description
Yuli is a film for which screenwriter Paul Laverty wrote the screenplay, likely reflecting his characteristic focus on socially conscious, character-driven storytelling.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yuli
Target entity description: Yuli is a film for which screenwriter Paul Laverty wrote the screenplay, likely reflecting his characteristic focus on socially conscious, character-driven storytelling.
  • A. Oksana
    Oksana is a feminine given name of Ukrainian origin, most famously borne by Olympic champion figure skater Oksana Baiul.
  • B. Yelena
    Yelena is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Helen or Helena in English.
  • C. Yulia
    Yulia is a feminine given name, commonly used in Slavic countries as a form of the name Julia.
  • D. Yulia Makhalina
    Yulia Makhalina is a renowned Russian ballerina celebrated as a principal dancer of the Mariinsky Ballet, noted for her elegant classical technique and dramatic stage presence.
  • E. Tatjana
    Tatjana is a feminine given name, commonly used in various European countries as a variant of Tatyana.
  • 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_69d6aa9a40d88190a373e2c7e48285db completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d799e844b08190987c7c8e8d626510 completed April 9, 2026, 12:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3e7b68ca88190a26ee54eb873c9cf completed April 18, 2026, 8:21 p.m.
NEDg Description generation batch_69e3f2cafc008190a3504999297f1e4e completed April 18, 2026, 9:08 p.m.
NED2 Entity disambiguation (via description) batch_69e3f488819081908f9a4225279cde6b completed April 18, 2026, 9:15 p.m.
Created at: April 8, 2026, 9:27 p.m.