Triple

T11997154
Position Surface form Disambiguated ID Type / Status
Subject Lady Montague E285560 entity
Predicate child P120 FINISHED
Object Romeo E54683 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 | Statement: [Lady Montague, child, Romeo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Romeo
Context triple: [Lady Montague, child, Romeo]
  • A. Romeo
    Romeo is a small statutory town located in Conejos County in southern Colorado, United States.
  • B. Romeo
    Romeo is a recurring mad-scientist villain in the children's animated superhero series PJ Masks, known for his inventive gadgets and schemes to outsmart the heroes.
  • C. Romeo Montague chosen
    Romeo Montague is the passionate young lover and tragic protagonist of William Shakespeare’s play "Romeo and Juliet," whose forbidden romance ends in mutual death.
  • D. Giulietta
    Giulietta is the given name of Countess Giulietta Guicciardi, an aristocratic woman best known as one of Ludwig van Beethoven’s piano students and the dedicatee of his "Moonlight" Sonata.
  • E. Capulet
    Capulet is the aristocratic family in Shakespeare's "Romeo and Juliet" that serves as one half of the play's central feuding houses.
  • 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_69d6ab44a77c8190a652f4b27164e4ef completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903c172788190b92042e9d10a48bf completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f60a5ccac481908c61fc1109cc6ef6 completed May 2, 2026, 2:29 p.m.
Created at: April 8, 2026, 9:46 p.m.