Triple
T20521701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Zaremba |
E503823
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | The Magnetic Monster |
—
|
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: The Magnetic Monster | Statement: [John Zaremba, notableWork, The Magnetic Monster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Magnetic Monster Context triple: [John Zaremba, notableWork, The Magnetic Monster]
-
A.
The Magnetic Monster
chosen
The Magnetic Monster is a 1953 science fiction film about scientists battling a rapidly growing, energy-absorbing artificial element that threatens to destroy the Earth.
-
B.
The Magnet
The Magnet is a 1950 British comedy film, often noted for its whimsical portrayal of childhood and moral dilemmas, directed by Charles Frend.
-
C.
The Monster Maker
The Monster Maker is a 1944 American horror film featuring Ralph Morgan in a prominent role as a mad scientist involved in grotesque experiments.
-
D.
The Iron Monster
"The Iron Monster" is an episode title from the 1939 science-fiction movie serial *The Phantom Creeps*, which starred Bela Lugosi as a mad scientist.
-
E.
The Blue Monster
The Blue Monster is a famously challenging golf course known for its long layout, water hazards, and prominent role in professional tournaments.
- 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_69e0b4b3a6e08190ae663701f50fab8e |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69f46488c819093687b4e07837793 |
completed | April 20, 2026, 9:48 p.m. |
Created at: April 16, 2026, 11:36 a.m.