Triple

T975387
Position Surface form Disambiguated ID Type / Status
Subject Louis Armstrong E21040 entity
Predicate spouse P13 FINISHED
Object Daisy Parker E33317 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: Daisy Parker | Statement: [Louis Armstrong, spouse, Daisy Parker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daisy Parker
Context triple: [Louis Armstrong, spouse, Daisy Parker]
  • A. Daisy Parker chosen
    Daisy Parker was the first wife of legendary jazz trumpeter and singer Louis Armstrong, whom he married in the early 1920s.
  • B. Lily Bell
    Lily Bell is a central character in the television series "Hell on Wheels," portrayed as a determined and resourceful Englishwoman navigating the dangers and politics surrounding the construction of the transcontinental railroad.
  • C. Millie Crocker-Harris
    Millie Crocker-Harris is a central character in Terence Rattigan’s play "The Browning Version," known as the embittered and unfaithful wife of the aging schoolmaster Andrew Crocker-Harris.
  • D. Binkie Beaumont
    Binkie Beaumont was a prominent British theatrical producer and manager known for his influential role in mid-20th-century West End theatre.
  • E. Ruth Rose
    Ruth Rose was an American screenwriter best known for co-writing the classic 1933 monster film "King Kong."
  • 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_69a493c2b62c8190b616351789ec47f8 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b46234c88190b2bfc9cafe59d7f7 completed March 1, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac4289acc88190886ac8971297b1f8 completed March 7, 2026, 3:21 p.m.
Created at: March 1, 2026, 7:40 p.m.