Triple
T13167926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tosa Domain |
E312901
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object |
Tosa
Tosa was a historical province on the southern coast of Japan’s Shikoku Island, corresponding largely to modern Kōchi Prefecture.
|
E1025428
|
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: Tosa | Statement: [Tosa Domain, region, Tosa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tosa Context triple: [Tosa Domain, region, Tosa]
-
A.
Usuki
Usuki is a historic coastal city in Japan known for its well-preserved samurai district and famous stone Buddha statues.
-
B.
Omura
Omura is a coastal city in western Japan known for its proximity to Nagasaki, Omura Bay, and its regional industrial and transportation hubs.
-
C.
Maruim
Maruim is a municipality in the Brazilian state of Sergipe, located along the Sergipe River and known for its historical and regional cultural significance.
-
D.
Tomia
Tomia is an island in Indonesia’s Wakatobi archipelago, renowned for its pristine coral reefs and exceptional scuba diving and snorkeling sites.
-
E.
Amakusa
Amakusa is a group of islands and a city in Kumamoto Prefecture, Japan, known for its coastal scenery, historical Christian heritage, and fishing communities.
- 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: Tosa Triple: [Tosa Domain, region, Tosa]
Generated description
Tosa was a historical province on the southern coast of Japan’s Shikoku Island, corresponding largely to modern Kōchi Prefecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tosa Target entity description: Tosa was a historical province on the southern coast of Japan’s Shikoku Island, corresponding largely to modern Kōchi Prefecture.
-
A.
Usuki
Usuki is a historic coastal city in Japan known for its well-preserved samurai district and famous stone Buddha statues.
-
B.
Omura
Omura is a coastal city in western Japan known for its proximity to Nagasaki, Omura Bay, and its regional industrial and transportation hubs.
-
C.
Maruim
Maruim is a municipality in the Brazilian state of Sergipe, located along the Sergipe River and known for its historical and regional cultural significance.
-
D.
Tomia
Tomia is an island in Indonesia’s Wakatobi archipelago, renowned for its pristine coral reefs and exceptional scuba diving and snorkeling sites.
-
E.
Amakusa
Amakusa is a group of islands and a city in Kumamoto Prefecture, Japan, known for its coastal scenery, historical Christian heritage, and fishing communities.
- 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_69d806ac3ee081909b2fd27d060aa974 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c2c317881908cc715c97d915f77 |
completed | April 10, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6eaf92c1881909d387dcf50d8d09f |
completed | May 3, 2026, 6:28 a.m. |
| NEDg | Description generation | batch_69f6f11b77a081909ea2ddedbac5abb8 |
completed | May 3, 2026, 6:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6f1b5c67c8190a2216ef32c5420c9 |
completed | May 3, 2026, 6:56 a.m. |
Created at: April 9, 2026, 9:13 p.m.