Triple

T1070882
Position Surface form Disambiguated ID Type / Status
Subject Jarma Lewis E23324 entity
Predicate notableWork P4 FINISHED
Object The Eternal Sea E114630 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: The Eternal Sea | Statement: [Jarma Lewis, notableWork, The Eternal Sea]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The Eternal Sea
Context triple: [Jarma Lewis, notableWork, The Eternal Sea]
  • A. The Eternal Sea chosen
    The Eternal Sea is a 1955 American war drama film starring Sterling Hayden as a determined naval officer coping with the loss of his leg during World War II.
  • B. The Sea
    The Sea is a painting by British artist L. S. Lowry, known for its minimalist seascape composition and characteristic muted palette.
  • C. Sea of Ice
    Sea of Ice is the English translation of "Mer de Glace," the largest and one of the most famous glaciers in the French Alps near Chamonix.
  • D. The Toll of the Sea
    The Toll of the Sea is a 1922 silent drama film, one of the earliest Hollywood movies shot in Technicolor, featuring Anna May Wong in her first leading role.
  • E. The Deep
    The Deep is a 1976 adventure novel by Peter Benchley that follows a young couple who discover dangerous secrets and sunken treasure while diving near Bermuda.
  • 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_69a493ee1f908190992b5f0d1b04459b completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b92848148190ba8795cb8d0a1d0a completed March 1, 2026, 10:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac42a70ab88190ba0f0a62b19af871 completed March 7, 2026, 3:22 p.m.
Created at: March 1, 2026, 7:42 p.m.