Triple

T3961465
Position Surface form Disambiguated ID Type / Status
Subject Traps E85912 entity
Predicate hasTitle P38 FINISHED
Object Traps E85912 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: Traps | Statement: [Traps, hasTitle, Traps]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Traps
Context triple: [Traps, hasTitle, Traps]
  • A. Traps chosen
    "Traps" is a novel by MacKenzie Scott (formerly MacKenzie Bezos), known for its interwoven narratives about four women whose lives collide over a tense four-day period.
  • 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. Trapped
    Trapped is a 2002 American thriller film produced by Mandalay Pictures, centered on a family's harrowing kidnapping ordeal and their desperate attempts to outwit their captors.
  • D. The Trick
    The Trick is a British drama film in which George MacKay stars in a story inspired by the real-life "Climategate" email hacking scandal.
  • 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_69aed93a96908190bcbdbfa718f155bd completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aef9617adc8190874b97a612fa7c72 completed March 9, 2026, 4:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b533b8e00c819093af24f27eee793b completed March 14, 2026, 10:08 a.m.
Created at: March 9, 2026, 3:31 p.m.