Triple
T17564885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Honey, I Shrunk the Kids |
E427784
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Rick Moranis |
—
|
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: Rick Moranis | Statement: [Honey, I Shrunk the Kids, stars, Rick Moranis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rick Moranis Context triple: [Honey, I Shrunk the Kids, stars, 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 (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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4592ce42c8190a54a0a328c5e8ffc |
completed | April 19, 2026, 4:25 a.m. |
Created at: April 10, 2026, 5:50 a.m.