Triple

T20806748
Position Surface form Disambiguated ID Type / Status
Subject Take All My Loves: 9 Shakespeare Sonnets E512180 entity
Predicate hasPart P35 FINISHED
Object Sonnet 29 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 29 | Statement: [Take All My Loves: 9 Shakespeare Sonnets, hasPart, Sonnet 29]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sonnet 29
Context triple: [Take All My Loves: 9 Shakespeare Sonnets, hasPart, Sonnet 29]
  • A. Sonnet 29 chosen
    Sonnet 29 is one of William Shakespeare’s most famous sonnets, known for its shift from deep despair to emotional renewal through the thought of a beloved friend.
  • B. Sonnet 30
    Sonnet 30 is one of William Shakespeare’s most famous sonnets, reflecting on themes of memory, loss, and the consoling power of friendship.
  • C. Sonnet 34
    Sonnet 34 is one of Edmund Spenser’s Amoretti sonnets, reflecting his characteristic blend of intricate poetic form and Renaissance love themes.
  • 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 130
    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.
  • 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_69e0b4cc69f481908e98751e697b9df4 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2cfdee481908e42a1a8940ea40c completed April 21, 2026, 12:20 a.m.
Created at: April 16, 2026, 12:40 p.m.