Triple

T19796941
Position Surface form Disambiguated ID Type / Status
Subject Peter Howard E475567 entity
Predicate knownAs P39 FINISHED
Object Peter Howard 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: Peter Howard | Statement: [Peter Howard, knownAs, Peter Howard]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peter Howard
Context triple: [Peter Howard, knownAs, Peter Howard]
  • A. Peter Howard
    Peter Howard was a prominent American musical theatre orchestrator and dance music arranger known for his work on numerous Broadway productions.
  • B. Peter Howard
    Peter Howard was a British journalist, playwright, and leader in the Moral Re-Armament movement, known for his politically charged writings and Christian-influenced social activism in the mid-20th century.
  • C. Ken Howard
    Ken Howard was an American actor best known for his roles in film, television, and theater, including his portrayal of Thomas Jefferson in the musical film "1776."
  • D. Scott Howard
    Scott Howard is the teenage protagonist of the 1985 comedy film "Teen Wolf," who discovers he has inherited the ability to transform into a werewolf.
  • E. Peter Davis
    Peter Davis is a New Zealand academic and sociologist best known as the husband of former Prime Minister Helen Clark.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide. chosen

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_69d8e51b014081908b263e167370529a completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e653c723548190ac9bfaecaf8afb13 completed April 20, 2026, 4:26 p.m.
Created at: April 10, 2026, 1:49 p.m.