Triple
T1595917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shiga |
E34281
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Hino
Hino is a town in Shiga Prefecture, Japan, known for its historical streetscapes and traditional industries.
|
E181516
|
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: Hino | Statement: [Shiga, hasCity, Hino]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hino Context triple: [Shiga, hasCity, Hino]
-
A.
Hino Motors, Ltd.
Hino Motors, Ltd. is a Japanese manufacturer specializing in commercial vehicles and diesel engines, known particularly for its trucks and buses.
-
B.
Isuzu
Isuzu is a Japanese automotive manufacturer best known for producing commercial vehicles, pickup trucks, and diesel engines for global markets.
-
C.
Nissan NV400
The Nissan NV400 is a large light commercial van developed in partnership with Renault and Opel/Vauxhall, sharing its platform with the Renault Master.
-
D.
Mitsubishi
Mitsubishi is a major Japanese multinational conglomerate known for its diverse businesses in industries such as automotive, heavy industry, finance, and electronics.
-
E.
Toyota T100
The Toyota T100 is a full-size pickup truck produced by Toyota in the 1990s that served as the brand’s early entry into the North American full-size truck market.
- 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: Hino Triple: [Shiga, hasCity, Hino]
Generated description
Hino is a town in Shiga Prefecture, Japan, known for its historical streetscapes and traditional industries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hino Target entity description: Hino is a town in Shiga Prefecture, Japan, known for its historical streetscapes and traditional industries.
-
A.
Hino Motors, Ltd.
Hino Motors, Ltd. is a Japanese manufacturer specializing in commercial vehicles and diesel engines, known particularly for its trucks and buses.
-
B.
Isuzu
Isuzu is a Japanese automotive manufacturer best known for producing commercial vehicles, pickup trucks, and diesel engines for global markets.
-
C.
Nissan NV400
The Nissan NV400 is a large light commercial van developed in partnership with Renault and Opel/Vauxhall, sharing its platform with the Renault Master.
-
D.
Mitsubishi
Mitsubishi is a major Japanese multinational conglomerate known for its diverse businesses in industries such as automotive, heavy industry, finance, and electronics.
-
E.
Toyota T100
The Toyota T100 is a full-size pickup truck produced by Toyota in the 1990s that served as the brand’s early entry into the North American full-size truck market.
- 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9092ccb388190b2f3ed86b3853651 |
completed | March 5, 2026, 4:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad46a848ec819085c82be8eaea2044 |
completed | March 8, 2026, 9:51 a.m. |
| NEDg | Description generation | batch_69ad4841d278819085507528faeaae3e |
completed | March 8, 2026, 9:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad48ff11d881909fd6e9e40d5f1f38 |
completed | March 8, 2026, 10:01 a.m. |
Created at: March 4, 2026, 7:27 p.m.