Triple
T1460171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mario |
E31492
|
entity |
| Predicate | powerUp |
P29028
|
FINISHED |
| Object |
Tanooki Suit
The Tanooki Suit is a special raccoon-dog-themed power-up in the Super Mario series that lets Mario fly, tail-whip enemies, and temporarily turn into a statue.
|
E167610
|
NE FINISHED |
How this triple was built (4 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: Tanooki Suit | Statement: [Mario, powerUp, Tanooki Suit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tanooki Suit Context triple: [Mario, powerUp, Tanooki Suit]
-
A.
Abaporu
Abaporu is a famous 1928 painting by Brazilian artist Tarsila do Amaral that became an icon of Brazilian modernism and inspired the Anthropophagic Movement in Brazilian art and literature.
-
B.
Marichi
Marichi is a revered Vedic sage (one of the Saptarishi) regarded as a mind-born son of Brahma and an important progenitor in Hindu cosmology.
-
C.
Simbo
Simbo is an Oceanic language of the Meso-Melanesian subgroup spoken on Simbo Island in the Solomon Islands.
-
D.
Lo-Toga
Lo-Toga is an Oceanic language spoken on the Torres Islands in northern Vanuatu.
-
E.
Sukki
Sukki is one of the four snowman mascots created to represent the 1998 Winter Olympics in Nagano, Japan.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tanooki Suit Triple: [Mario, powerUp, Tanooki Suit]
Generated description
The Tanooki Suit is a special raccoon-dog-themed power-up in the Super Mario series that lets Mario fly, tail-whip enemies, and temporarily turn into a statue.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tanooki Suit Target entity description: The Tanooki Suit is a special raccoon-dog-themed power-up in the Super Mario series that lets Mario fly, tail-whip enemies, and temporarily turn into a statue.
-
A.
Abaporu
Abaporu is a famous 1928 painting by Brazilian artist Tarsila do Amaral that became an icon of Brazilian modernism and inspired the Anthropophagic Movement in Brazilian art and literature.
-
B.
Marichi
Marichi is a revered Vedic sage (one of the Saptarishi) regarded as a mind-born son of Brahma and an important progenitor in Hindu cosmology.
-
C.
Simbo
Simbo is an Oceanic language of the Meso-Melanesian subgroup spoken on Simbo Island in the Solomon Islands.
-
D.
Lo-Toga
Lo-Toga is an Oceanic language spoken on the Torres Islands in northern Vanuatu.
-
E.
Sukki
Sukki is one of the four snowman mascots created to represent the 1998 Winter Olympics in Nagano, Japan.
- F. None of above. chosen
Provenance (5 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c9e02c188190b87c0aac939eafdd |
completed | March 1, 2026, 11:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad0e786a208190a57c4e1878c66517 |
completed | March 8, 2026, 5:51 a.m. |
| NEDg | Description generation | batch_69ad0ee93c4c8190bd705e31d9492158 |
completed | March 8, 2026, 5:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad0fb331e881908455844135bb3208 |
completed | March 8, 2026, 5:57 a.m. |
Created at: March 1, 2026, 8 p.m.