Triple

T4514797
Position Surface form Disambiguated ID Type / Status
Subject Volume I E102128 entity
Predicate introducesCharacter P12208 FINISHED
Object Sonya E118325 NE FINISHED

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: Sonya | Statement: [Volume I, introducesCharacter, Sonya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sonya
Context triple: [Volume I, introducesCharacter, Sonya]
  • A. Sonya chosen
    Sonya is a gentle, selfless young woman in Leo Tolstoy’s novel "War and Peace," known for her unrequited love and quiet loyalty to the Rostov family.
  • B. Sonia
    Sonia is a central female character in the romantic comedy film "Think Like a Man," whose relationships and personal growth intersect with the movie’s ensemble cast and themes about modern dating.
  • C. Sonia
    Sonia is the given name of Sonia Gandhi, an Italian-born Indian politician and former president of the Indian National Congress.
  • D. Natalya
    Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
  • E. Aloysya
    Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69bd43d6251c81909deecce3e6e9d69c completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd5725745c81908bb462ba9537ae14 completed March 20, 2026, 2:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdacd174c88190a186bc2ecc20fcd5 completed March 20, 2026, 8:23 p.m.
Created at: March 20, 2026, 1:02 p.m.