Triple

T9616173
Position Surface form Disambiguated ID Type / Status
Subject Honey, I Shrunk the Audience E232224 entity
Predicate featuresActor P15562 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: [Honey, I Shrunk the Audience, featuresActor, Rick Moranis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rick Moranis
Context triple: [Honey, I Shrunk the Audience, featuresActor, 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_69ca84867bb88190b4b57dd5a56d5691 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9aad71a0819084ea00c2409e9922 completed April 1, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1795e7be08190a088e49a79251570 completed April 4, 2026, 8:49 p.m.
Created at: March 30, 2026, 8:09 p.m.