Triple

T11988226
Position Surface form Disambiguated ID Type / Status
Subject Michael Abels E285337 entity
Predicate composedFor P30143 FINISHED
Object All Day and a Night E590008 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: All Day and a Night | Statement: [Michael Abels, composedFor, All Day and a Night]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: All Day and a Night
Context triple: [Michael Abels, composedFor, All Day and a Night]
  • A. All Day and a Night chosen
    All Day and a Night is a crime thriller novel by Alafair Burke featuring a complex murder case that intertwines past and present investigations.
  • B. All Through the Night
    All Through the Night is a mystery novel by Mary Higgins Clark featuring her recurring amateur sleuths Alvirah and Willy Meehan.
  • C. All Through the Night
    All Through the Night is a 1942 wartime comedy-thriller film starring Humphrey Bogart as a New York gambler who uncovers a Nazi spy ring.
  • D. All Through the Night
    "All Through the Night" is a 1983 pop ballad popularized by Cyndi Lauper, known for its dreamy synth-driven sound and emotional vocals.
  • E. All Through the Night
    "All Through the Night" is a work by German writer Käthe Vörnle, best known as part of her literary output in the early 20th century.
  • 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_69d903ae28708190a826bad1624343eb completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f472492ebc8190b064e691bb70e356 completed May 1, 2026, 9:28 a.m.
Created at: April 8, 2026, 9:46 p.m.