Triple

T3240119
Position Surface form Disambiguated ID Type / Status
Subject Enough Said E67945 entity
Predicate mainCharacter P1183 FINISHED
Object Eva E93610 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: Eva | Statement: [Enough Said, mainCharacter, Eva]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eva
Context triple: [Enough Said, mainCharacter, Eva]
  • A. Eva chosen
    Eva is a feminine given name of Hebrew origin, equivalent to "Eve" and widely used in many languages and cultures.
  • B. Evelyn
    Evelyn is a given name shared by G. Evelyn Hutchinson, a prominent 20th-century British-born American ecologist often called the "father of modern ecology."
  • C. Eva Peace
    Eva Peace is a fiercely independent, sharp-tongued matriarch in Toni Morrison’s novel "Sula," known for her unconventional life, physical disability, and complex relationship with her children and community.
  • D. Malena
    Malena is a feminine given name, commonly used in various cultures and often considered a diminutive or variant of names like Magdalena.
  • E. Marlene
    Marlene is a German biographical film directed by Joseph Vilsmaier about the life and career of actress and singer Marlene Dietrich.
  • 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_69ad858d27348190abb61c280b4c86a9 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaef6430081909084589f6eea5c7e completed March 8, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2774f93448190b8493b457636ae48 completed March 12, 2026, 8:20 a.m.
Created at: March 8, 2026, 3:08 p.m.