Triple

T16429137
Position Surface form Disambiguated ID Type / Status
Subject Jack Moran E399024 entity
Predicate portrayedBy P1507 FINISHED
Object Rick Moranis E270496 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: Rick Moranis | Statement: [Jack Moran, portrayedBy, Rick Moranis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rick Moranis
Context triple: [Jack Moran, portrayedBy, Rick Moranis]
  • A. Rick Moranis chosen
    Rick Moranis is a Canadian actor and comedian best known for his roles in films such as Ghostbusters, Honey, I Shrunk the Kids, and Spaceballs.
  • B. Will Murray
    Will Murray is an American writer best known for his extensive work continuing classic pulp fiction series, particularly the Doc Savage novels.
  • C. Chris Elliott
    Chris Elliott is an American actor and comedian known for his offbeat roles in film and television, including his supporting role in the comedy classic "Groundhog Day."
  • D. Martin Short
    Martin Short is a Canadian-American comedian and actor renowned for his energetic characters and work on sketch comedy shows, films, and Broadway.
  • E. David Koechner
    David Koechner is an American character actor and comedian best known for his scene-stealing roles in films like Anchorman and the TV series The Office.
  • 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_69d87f2b9024819085c20e52de95d583 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e328fd49708190abb5065fa430eff1 completed April 18, 2026, 6:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00458331748190a4bd1c5d2d466e6d completed May 10, 2026, 8:44 a.m.
Created at: April 10, 2026, 5:09 a.m.