Triple
T16744593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Yōtei |
E406918
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Makkari
Makkari is a small village in Hokkaido, Japan, known for its rural scenery and proximity to the iconic volcanic peak Mount Yōtei.
|
E1236415
|
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: Makkari | Statement: [Mount Yōtei, locatedNear, Makkari]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Makkari Context triple: [Mount Yōtei, locatedNear, Makkari]
-
A.
Makkari
Makkari is a super-speed-powered Eternal and one of the central immortal heroes featured in the Marvel Cinematic Universe film "Eternals."
-
B.
Mesharu
Mesharu is a minor Mesopotamian deity associated with justice and righteousness, traditionally regarded as a child of the sun god Shamash.
-
C.
Maki Ziro
Maki Ziro is a Japanese physicist known as a prominent student and collaborator of theoretical physicist Shoichi Sakata.
-
D.
Mamoru
Mamoru is a Japanese masculine given name commonly borne by notable figures in politics, arts, and entertainment.
-
E.
Mako Kamitsuna
Mako Kamitsuna is a Japanese-born film editor and filmmaker known for her work on critically acclaimed independent films, including the period drama "Mudbound."
- 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: Makkari Triple: [Mount Yōtei, locatedNear, Makkari]
Generated description
Makkari is a small village in Hokkaido, Japan, known for its rural scenery and proximity to the iconic volcanic peak Mount Yōtei.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Makkari Target entity description: Makkari is a small village in Hokkaido, Japan, known for its rural scenery and proximity to the iconic volcanic peak Mount Yōtei.
-
A.
Makkari
Makkari is a super-speed-powered Eternal and one of the central immortal heroes featured in the Marvel Cinematic Universe film "Eternals."
-
B.
Mesharu
Mesharu is a minor Mesopotamian deity associated with justice and righteousness, traditionally regarded as a child of the sun god Shamash.
-
C.
Maki Ziro
Maki Ziro is a Japanese physicist known as a prominent student and collaborator of theoretical physicist Shoichi Sakata.
-
D.
Mamoru
Mamoru is a Japanese masculine given name commonly borne by notable figures in politics, arts, and entertainment.
-
E.
Mako Kamitsuna
Mako Kamitsuna is a Japanese-born film editor and filmmaker known for her work on critically acclaimed independent films, including the period drama "Mudbound."
- 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_69d8838ffb088190a0b11149929006bf |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3aa210ef88190be74bd60d7144953 |
completed | April 18, 2026, 3:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00bb069cf481908e029b26ad96d3b5 |
completed | May 10, 2026, 5:06 p.m. |
| NEDg | Description generation | batch_6a00bc136bfc8190ab93cd8e0e7eaf1c |
completed | May 10, 2026, 5:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00bca0a3808190be3d1d7ebd77cc20 |
completed | May 10, 2026, 5:13 p.m. |
Created at: April 10, 2026, 5:21 a.m.