Triple
T17841309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gamera: Super Monster |
E445533
|
entity |
| Predicate | featuresMonster |
P18264
|
FINISHED |
| Object | Barugon |
—
|
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: Barugon | Statement: [Gamera: Super Monster, featuresMonster, Barugon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barugon Context triple: [Gamera: Super Monster, featuresMonster, Barugon]
-
A.
Barugon
chosen
Barugon is a giant kaiju from the Gamera film series, known for its lizard-like appearance and deadly freezing and rainbow-beam abilities.
-
B.
Baragon
Baragon is a burrowing, horned kaiju from the Godzilla franchise, known for its subterranean attacks and appearances in classic Toho monster films.
-
C.
Boorga
Boorga is a small rural locality within the Hay Shire local government area in New South Wales, Australia.
-
D.
Balardgon
Balardgon is the nickname for the French Ministry of the Armed Forces’ large, modern military headquarters complex located in the Balard area of Paris.
-
E.
Nugaal
Nugaal is a region in northeastern Somalia known for its arid landscape and pastoralist communities.
- 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_69d8b9f1a6d881909f024bc603111cdb |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48d2b2ea08190926ec0cf01285833 |
completed | April 19, 2026, 8:07 a.m. |
Created at: April 10, 2026, 10:16 a.m.