Triple

T18206452
Position Surface form Disambiguated ID Type / Status
Subject Lenina Crowne E435914 entity
Predicate usesDrug P36323 FINISHED
Object Soma 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: Soma | Statement: [Lenina Crowne, usesDrug, Soma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Soma
Context triple: [Lenina Crowne, usesDrug, Soma]
  • A. Soma chosen
    Soma is a Vedic deity associated with the sacred ritual drink, the Moon, and divine inspiration in ancient Indian religion.
  • B. Soma
    Soma is an immersive large-scale installation by artist Carsten Höller that explores altered perception, consciousness, and the boundary between reality and experiment.
  • C. Sulien
    Sulien is a Welsh saint traditionally venerated as a local holy figure associated with churches in Wales.
  • D. Sikma
    Sikma is a surname most notably associated with Jack Sikma, a Hall of Fame American basketball player known for his successful NBA career with the Seattle SuperSonics.
  • E. Tetro
    Tetro is a 2009 drama film directed by Francis Ford Coppola, in which Maribel Verdú plays a key supporting role in a story about fractured family relationships and artistic rivalry in Buenos Aires.
  • 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_69d8b90dba6481908e119eb9aa4ca0cb completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4e2234b988190bbe2c2164d61f65f completed April 19, 2026, 2:09 p.m.
Created at: April 10, 2026, 10:32 a.m.