Triple
T20262810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Super Mario Land |
E498886
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Sarasaland |
—
|
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: Sarasaland | Statement: [Super Mario Land, setting, Sarasaland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarasaland Context triple: [Super Mario Land, setting, Sarasaland]
-
A.
Sarasaland
chosen
Sarasaland is a fictional desert- and pyramid-themed kingdom in the Super Mario series, primarily featured as Princess Daisy’s homeland in Super Mario Land.
-
B.
Borispol
Borispol is an alternative transliteration of Boryspil, a Ukrainian city near Kyiv best known for hosting the country’s main international airport.
-
C.
Vorkuta
Vorkuta is a remote Arctic city in Russia historically known as one of the largest centers of the Soviet Gulag labor camp system.
-
D.
Berdiansk
Berdiansk is a port city on the northern coast of the Sea of Azov in southeastern Ukraine, known for its beaches and resort facilities.
-
E.
Nevelsk
Nevelsk is a small port town on the southwest coast of Sakhalin Island in Russia, facing the Sea of Japan.
- 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e674cba2748190a886ecd8316dc518 |
completed | April 20, 2026, 6:47 p.m. |
Created at: April 11, 2026, 11:41 p.m.