Triple
T3580062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mario Kart Wii |
E75777
|
entity |
| Predicate | includesCharacter |
P5716
|
FINISHED |
| Object |
Mii
Mii is a customizable avatar character created by players on Nintendo consoles and used across various games as a personal in-game representation.
|
E370373
|
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: Mii | Statement: [Mario Kart Wii, includesCharacter, Mii]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mii Context triple: [Mario Kart Wii, includesCharacter, Mii]
-
A.
Miki
Miki is a city in Japan located within Hyogo Prefecture, known for its traditional hardware industry and historical sites.
-
B.
Taitō
Taitō is a special ward in central Tokyo known for its historic districts, traditional temples, and major cultural attractions such as Ueno Park and Asakusa.
-
C.
Mio
Mio is the Japanese-led Mercury Magnetospheric Orbiter, a component of the joint ESA–JAXA BepiColombo mission designed to study Mercury’s magnetic field and space environment.
-
D.
Ga Mashie
Ga Mashie is a historic coastal community in Accra, Ghana, regarded as the traditional heartland and cultural center of the Ga people.
-
E.
Wiig
Wiig is the surname of American actress, comedian, and writer Kristen Wiig, known for her work on Saturday Night Live and in films like Bridesmaids.
- 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: Mii Triple: [Mario Kart Wii, includesCharacter, Mii]
Generated description
Mii is a customizable avatar character created by players on Nintendo consoles and used across various games as a personal in-game representation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mii Target entity description: Mii is a customizable avatar character created by players on Nintendo consoles and used across various games as a personal in-game representation.
-
A.
Miki
Miki is a city in Japan located within Hyogo Prefecture, known for its traditional hardware industry and historical sites.
-
B.
Taitō
Taitō is a special ward in central Tokyo known for its historic districts, traditional temples, and major cultural attractions such as Ueno Park and Asakusa.
-
C.
Mio
Mio is the Japanese-led Mercury Magnetospheric Orbiter, a component of the joint ESA–JAXA BepiColombo mission designed to study Mercury’s magnetic field and space environment.
-
D.
Ga Mashie
Ga Mashie is a historic coastal community in Accra, Ghana, regarded as the traditional heartland and cultural center of the Ga people.
-
E.
Wiig
Wiig is the surname of American actress, comedian, and writer Kristen Wiig, known for her work on Saturday Night Live and in films like Bridesmaids.
- 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_69ad85d5e3008190bdfe0bacdd1f5a1b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0ffecdc8190bf01c8ba90e3733e |
completed | March 8, 2026, 6:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bbc95fa881909846a6d53ba6a24e |
completed | March 13, 2026, 7:24 a.m. |
| NEDg | Description generation | batch_69b3bcae84a48190b085f253773cd14f |
completed | March 13, 2026, 7:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3f90df47c81908855021f68ca7ec8 |
completed | March 13, 2026, 11:46 a.m. |
Created at: March 8, 2026, 3:21 p.m.