Triple

T309147
Position Surface form Disambiguated ID Type / Status
Subject George Lucas E6364 entity
Predicate produced P490 FINISHED
Object Willow E14248 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: Willow | Statement: [George Lucas, produced, Willow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Willow
Context triple: [George Lucas, produced, Willow]
  • A. Willow chosen
    Willow is a 1988 fantasy adventure film directed by Ron Howard that follows an aspiring sorcerer who must protect a prophesied child from an evil queen.
  • B. Magi
    The Magi are the wise men or kings from the East in the New Testament who visit the infant Jesus, traditionally bearing gifts of gold, frankincense, and myrrh.
  • C. Cocoon
    Cocoon is a 1985 science fiction comedy-drama film about a group of elderly people who regain youth and vitality after encountering alien life, directed by Ron Howard.
  • D. Xanadu
    Xanadu is the opulent, mythical pleasure-dome city evoked in Samuel Taylor Coleridge’s poem "Kubla Khan."
  • E. Into the Woods
    Into the Woods is a 2014 musical fantasy film adaptation of the Stephen Sondheim stage musical, intertwining several classic fairy tales in a darkly comedic and emotionally complex narrative.
  • 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_69a2e79230508190b912ecb555aae17e completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2ea33ba688190b30d285cd7aa0d82 completed Feb. 28, 2026, 1:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3b475e91c8190b68b05a8112d35dd completed March 1, 2026, 3:37 a.m.
Created at: Feb. 28, 2026, 1:06 p.m.