Triple

T13174849
Position Surface form Disambiguated ID Type / Status
Subject Slither E313072 entity
Predicate character P662 FINISHED
Object Grant Grant E748994 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: Grant Grant | Statement: [Slither, character, Grant Grant]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Grant Grant
Context triple: [Slither, character, Grant Grant]
  • A. Grant Grant chosen
    Grant Grant is a fictional character best known as the parasitically infected antagonist in the horror-comedy film "Slither."
  • B. Red Grant
    Red Grant is a ruthless, psychopathic assassin and primary antagonist in the James Bond franchise, most prominently appearing as SPECTRE’s top killer in the film and novel "From Russia, with Love."
  • C. Stan Grant Jr.
    Stan Grant Jr. is an Australian journalist, author, and television presenter known for his reporting on Indigenous issues and global affairs.
  • D. Arthur Grant
    Arthur Grant was a British cinematographer best known for his work on numerous Hammer Films productions in the mid-20th century.
  • E. Grant Grove
    Grant Grove is a renowned grove of giant sequoia trees in California’s Sierra Nevada, best known as the home of the General Grant Tree, one of the largest trees on Earth.
  • 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_69d806ac3ee081909b2fd27d060aa974 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98c303e3c819086cf0f0b6d9e61ca completed April 10, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6eafe03c48190992df41f77fb043e completed May 3, 2026, 6:28 a.m.
Created at: April 9, 2026, 9:14 p.m.