Triple

T12667555
Position Surface form Disambiguated ID Type / Status
Subject Colonel Tom Parker E302596 entity
Predicate alsoKnownAs P39 FINISHED
Object Tom Parker E994401 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: Tom Parker | Statement: [Colonel Tom Parker, alsoKnownAs, Tom Parker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tom Parker
Context triple: [Colonel Tom Parker, alsoKnownAs, Tom Parker]
  • A. Tom Parker chosen
    Tom Parker was the influential and controversial music manager best known for guiding Elvis Presley’s career to global superstardom.
  • B. Tom Parker
    Tom Parker is an enthusiastic and somewhat impulsive entrepreneur in Jane Austen’s unfinished novel "Sanditon," whose ambitions to develop the seaside resort drive much of the story’s events.
  • C. Michael Parker
    Michael Parker is a film editor best known for his work on the British comedy-drama "Made in Dagenham."
  • D. Steve Parker
    Steve Parker was an American film producer and manager best known for his long marriage to actress Shirley MacLaine and his work on international film projects.
  • E. Brad Parker
    Brad Parker is a screenwriter known for collaborating with Carey Van Dyke on film and television projects.
  • 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_69d7bded71a88190bb76e2413af9ea66 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96181c40481908f3e2717f5472b85 completed April 10, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6719de0908190bbc9a98e67e5b6eb completed May 2, 2026, 9:50 p.m.
Created at: April 9, 2026, 5:20 p.m.