Triple

T22217216
Position Surface form Disambiguated ID Type / Status
Subject David Spradley E549106 entity
Predicate notableWork P4 FINISHED
Object So Many Tears 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: So Many Tears | Statement: [David Spradley, notableWork, So Many Tears]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: So Many Tears
Context triple: [David Spradley, notableWork, So Many Tears]
  • A. So Many Tears chosen
    "So Many Tears" is a reflective and emotionally charged song by Tupac Shakur that explores themes of pain, loss, and inner turmoil.
  • B. So Many Tears
    "So Many Tears" is an R&B/soul song by American singer Regina Belle, showcasing her smooth vocals and emotive, romantic style.
  • C. Too Many Tears
    "Too Many Tears" is a country song recorded by American band Restless Heart, known for their smooth harmonies and melodic, radio-friendly sound.
  • D. No More Tears
    "No More Tears" is a song recorded by the artist known as The Songstress, showcasing her smooth, emotive R&B vocal style.
  • E. Too Late for Tears
    Too Late for Tears is a 1949 film noir thriller about a housewife corrupted by a sudden windfall of illicit cash, noted for Lizabeth Scott’s hard-edged, morally ambiguous performance.
  • 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_69e11e3f7e04819089806d81d5ac431e completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f12b8c9d488190a59f571862997304 completed April 28, 2026, 9:50 p.m.
Created at: April 16, 2026, 8:37 p.m.