Triple

T6500474
Position Surface form Disambiguated ID Type / Status
Subject Love Story E148870 entity
Predicate inspiredBy P9 FINISHED
Object Romeo and Juliet E62132 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: Romeo and Juliet | Statement: [Love Story, inspiredBy, Romeo and Juliet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Romeo and Juliet
Context triple: [Love Story, inspiredBy, Romeo and Juliet]
  • A. Romeo and Juliet chosen
    Romeo and Juliet is a tragic play by William Shakespeare about two young lovers from feuding families whose doomed relationship has become one of the most famous love stories in Western literature.
  • B. Romeo + Juliet
    Romeo + Juliet is a 1996 modernized film adaptation of Shakespeare’s tragedy, directed by Baz Luhrmann and starring Leonardo DiCaprio and Claire Danes as the titular lovers.
  • C. Star Crossed Lovers
    Star Crossed Lovers is a romantic-themed segment or track that explores the challenges and intensity of a doomed or fated love.
  • D. Lovers
    Lovers is a Spanish film featuring actress Maribel Verdú in one of her notable roles.
  • E. Romeo
    Romeo is a small statutory town located in Conejos County in southern Colorado, United States.
  • 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_69c687e9ad288190bae5bcac9c8ac855 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c68ad2e148819088be5c48ad73dc59 completed March 27, 2026, 1:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6cb26d2d08190a52084c3a8c0d8f8 completed March 27, 2026, 6:23 p.m.
Created at: March 27, 2026, 1:42 p.m.