Triple

T13735696
Position Surface form Disambiguated ID Type / Status
Subject Digi Snacks E329938 entity
Predicate hasTrack P3284 FINISHED
Object Booby Trap E1007195 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: Booby Trap | Statement: [Digi Snacks, hasTrack, Booby Trap]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Booby Trap
Context triple: [Digi Snacks, hasTrack, Booby Trap]
  • A. Booby Trap chosen
    "Booby Trap" is an episode of Star Trek: The Next Generation in which the crew of the Enterprise-D becomes ensnared in an ancient energy-draining trap while Geordi La Forge works with a holographic recreation of the ship’s designer to find a way out.
  • B. The Trap
    "The Trap" is a horror novel by Tabitha King that delves into psychological terror and the darker sides of human relationships in a small-town setting.
  • C. The Trap
    The Trap is a 1966 British adventure drama film set in the Canadian wilderness, starring Rita Tushingham and Oliver Reed.
  • D. Time Trap
    Time Trap is a 2017 science fiction adventure film about a group of students who discover a cave where time passes at drastically different speeds.
  • E. The Steel Trap
    The Steel Trap is a 1952 American crime thriller film starring Joseph Cotten as a bank employee who devises a plan to steal money and flee the country.
  • 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_69d80772315881908f980cae40d91664 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69de020351fc8190a554a48c552e83b5 completed April 14, 2026, 8:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69f79d68da04819089c95d9bfedc7496 completed May 3, 2026, 7:09 p.m.
Created at: April 9, 2026, 9:55 p.m.