Triple
T12332048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Perfect Storm |
E293984
|
entity |
| Predicate | hasSingle |
P3282
|
FINISHED |
| Object | Make a Movie |
E976714
|
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: Make a Movie | Statement: [The Perfect Storm, hasSingle, Make a Movie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Make a Movie Context triple: [The Perfect Storm, hasSingle, Make a Movie]
-
A.
Make a Movie
chosen
Make a Movie is a special feature that guides viewers through the filmmaking process behind the creation of "The Perfect Storm."
-
B.
Making Movies
Making Movies is a widely respected memoir and craft-focused book in which acclaimed film director Sidney Lumet explains his practical approach to filmmaking.
-
C.
Movima
Movima is an indigenous language of the Bolivian lowlands, spoken by the Movima people primarily in the Beni Department.
-
D.
Living at the Movies
Living at the Movies is a poetry collection by American writer and punk icon Jim Carroll, known for its vivid, streetwise depictions of urban life and youth.
-
E.
Film Begets Film
Film Begets Film is a critical study by film historian Jay Leyda that examines the influence of existing films on the creation and evolution of new cinematic works.
- 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_69d6ab6ae0dc8190b1522a9c1c55c114 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f634ee08190b4f533505d402219 |
completed | April 10, 2026, 6:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62aa12f108190851c6958eb35ee5b |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:53 p.m.