Triple

T20897959
Position Surface form Disambiguated ID Type / Status
Subject Felicity Rose Hadley Jones E514591 entity
Predicate notableWork P4 FINISHED
Object Inferno 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: Inferno | Statement: [Felicity Rose Hadley Jones, notableWork, Inferno]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Inferno
Context triple: [Felicity Rose Hadley Jones, notableWork, Inferno]
  • A. Inferno
    Inferno is the first cantica of Dante Alighieri’s Divine Comedy, depicting the poet’s allegorical journey through the nine circles of Hell.
  • B. Inferno
    "Inferno" is a 1953 Technicolor 3D film noir thriller starring William Lundigan alongside Robert Ryan and Rhonda Fleming, noted for its desert survival plot and innovative use of 3D cinematography.
  • C. Inferno
    "Inferno" is a 1980s action thriller film best known for its desert survival and revenge storyline, directed by John G. Avildsen.
  • D. Inferno
    Inferno is a major expansion for the sci-fi MMORPG EVE Online that focused on revamping warfare mechanics, including factional warfare and mercenary contracts.
  • E. Inferno chosen
    "Inferno" is a 2016 mystery thriller film based on Dan Brown's novel, in which Irrfan Khan plays a key supporting role alongside Tom Hanks.
  • 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_69e0b4f7ebe48190952a85547a0f31a1 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6e8f826788190b11008cc94b2a4e4 completed April 21, 2026, 3:03 a.m.
Created at: April 16, 2026, 12:47 p.m.