Triple
T15496499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokoname |
E378830
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object |
Tokai
Tokai is a city in Aichi Prefecture, Japan, known for its industrial base and proximity to the Nagoya metropolitan area.
|
E1162879
|
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: Tokai | Statement: [Tokoname, borders, Tokai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tokai Context triple: [Tokoname, borders, Tokai]
-
A.
Tokai
Tokai is a suburb in Cape Town, South Africa, known for its residential areas, green spaces, and proximity to the Constantiaberg mountains.
-
B.
Tenri
Tenri is a small city in Japan known as the headquarters of the Tenrikyo religion and for its rich archaeological and cultural heritage.
-
C.
Tomonoura
Tomonoura is a historic port town in Hiroshima Prefecture, Japan, known for its scenic seaside views, traditional streetscapes, and role as inspiration for various works of art and film.
-
D.
Fujieda
Fujieda is a city in Shizuoka Prefecture, Japan, known as a regional commercial center with a mix of residential areas, agriculture, and light industry.
-
E.
Shinano
Shinano was a Japanese World War II aircraft carrier, originally laid down as a Yamato-class battleship and notable for being the largest carrier ever sunk in combat.
- 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: Tokai Triple: [Tokoname, borders, Tokai]
Generated description
Tokai is a city in Aichi Prefecture, Japan, known for its industrial base and proximity to the Nagoya metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tokai Target entity description: Tokai is a city in Aichi Prefecture, Japan, known for its industrial base and proximity to the Nagoya metropolitan area.
-
A.
Tokai
Tokai is a suburb in Cape Town, South Africa, known for its residential areas, green spaces, and proximity to the Constantiaberg mountains.
-
B.
Tenri
Tenri is a small city in Japan known as the headquarters of the Tenrikyo religion and for its rich archaeological and cultural heritage.
-
C.
Tomonoura
Tomonoura is a historic port town in Hiroshima Prefecture, Japan, known for its scenic seaside views, traditional streetscapes, and role as inspiration for various works of art and film.
-
D.
Fujieda
Fujieda is a city in Shizuoka Prefecture, Japan, known as a regional commercial center with a mix of residential areas, agriculture, and light industry.
-
E.
Shinano
Shinano was a Japanese World War II aircraft carrier, originally laid down as a Yamato-class battleship and notable for being the largest carrier ever sunk in combat.
- 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03faecd60819091eeaa56c9c8f67d |
completed | April 16, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff454ca0f0819088ba846a448dda2e |
completed | May 9, 2026, 2:31 p.m. |
| NEDg | Description generation | batch_69ff466b2f58819098c98b44548f4ce9 |
completed | May 9, 2026, 2:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff46a192288190b7fd6db18bfc1fca |
completed | May 9, 2026, 2:37 p.m. |
Created at: April 10, 2026, 3:52 a.m.