Triple

T19980945
Position Surface form Disambiguated ID Type / Status
Subject Dark Lady E493813 entity
Predicate hasNotableWork P4 FINISHED
Object Sonnet 130 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: Sonnet 130 | Statement: [Dark Lady, hasNotableWork, Sonnet 130]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sonnet 130
Context triple: [Dark Lady, hasNotableWork, Sonnet 130]
  • A. Sonnet 130 chosen
    Sonnet 130 is one of William Shakespeare’s most famous sonnets, noted for its ironic, realistic portrayal of the speaker’s mistress that subverts conventional poetic idealization of beauty.
  • B. Sonnet 138
    Sonnet 138 is one of William Shakespeare’s best-known sonnets, notable for its ironic exploration of love, deception, and self-delusion in a mature romantic relationship.
  • C. Sonnet 67
    Sonnet 67 is one of Edmund Spenser’s Amoretti sonnets, notable for its allegorical depiction of love through the extended metaphor of a hunter and a deer.
  • D. Sonnet 129
    Sonnet 129 is one of William Shakespeare’s most famous sonnets, noted for its intense exploration of lust, guilt, and moral conflict.
  • E. Sonnet 94
    Sonnet 94 is one of William Shakespeare’s most famous and morally complex sonnets, often noted for its meditation on power, restraint, and corruption.
  • 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_69da626a67648190af9653832a3aeced completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e65d12d968819081e315ec4585cd9f completed April 20, 2026, 5:06 p.m.
Created at: April 11, 2026, 3:28 p.m.