Triple

T19872202
Position Surface form Disambiguated ID Type / Status
Subject Deathtrap (1982 film) E477545 entity
Predicate narrativeLocation P40 FINISHED
Object Connecticut 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: Connecticut | Statement: [Deathtrap (1982 film), narrativeLocation, Connecticut]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Connecticut
Context triple: [Deathtrap (1982 film), narrativeLocation, Connecticut]
  • A. Connecticut chosen
    Connecticut is a small New England state in the northeastern United States known for its colonial history, affluent suburbs, and role as a financial and educational hub.
  • B. D. Conn.
    D. Conn. is the standard legal abbreviation for the United States District Court for the District of Connecticut, a federal trial court within the Second Circuit.
  • C. Rhoda Island
    Rhoda Island is a Nile island in central Cairo, Egypt, known for its historic palaces, gardens, and cultural landmarks.
  • D. Georgia, Vermont
    Georgia, Vermont is a small rural town in northwestern Vermont known for its agricultural landscape and proximity to Lake Champlain.
  • E. Vermont
    Vermont is a small, rural New England state in the northeastern United States, known for its Green Mountains, maple syrup production, and picturesque towns.
  • 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_69d8e51e7d948190aedbcd6c30361c39 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e658d826f88190be04188997952d1b completed April 20, 2026, 4:48 p.m.
Created at: April 10, 2026, 1:51 p.m.