Triple

T17586152
Position Surface form Disambiguated ID Type / Status
Subject Senso E428326 entity
Predicate basedOn P98 FINISHED
Object Senso (novella) NE NERFINISHED

How this triple was built (2 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: Senso (novella) | Statement: [Senso, basedOn, Senso (novella)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Senso (novella)
Context triple: [Senso, basedOn, Senso (novella)]
  • A. Senso chosen
    Senso is a 1954 Italian historical melodrama film directed by Luchino Visconti, set during the Risorgimento and renowned for its lush visuals and tragic love story.
  • B. Sansei and Sensibility
    Sansei and Sensibility is a short story collection by Karen Tei Yamashita that blends Japanese American experiences with playful reimaginings of Jane Austen’s themes and characters.
  • C. Sutami
    Sutami was an Indonesian engineer and politician who served as Minister of Public Works and played a key role in the country’s infrastructure development.
  • D. Tosa Nikki
    Tosa Nikki is a 10th-century Japanese literary diary written in kana and traditionally attributed to Ki no Tsurayuki, often regarded as one of the earliest and most important examples of Japanese prose.
  • E. Shisō
    Shisō is a city in Hyōgo Prefecture, Japan, known for its rural landscapes, forests, and historical sites.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889e1030481909950e140c63255b9 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e463d22f908190ae0f1eeafbe54459 completed April 19, 2026, 5:10 a.m.
Created at: April 10, 2026, 5:50 a.m.