Triple
T11119921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Minamata disease |
E262988
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Minamata |
E791861
|
NE FINISHED |
How this triple was built (2 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: Minamata | Statement: [Minamata disease, namedAfter, Minamata]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Minamata Context triple: [Minamata disease, namedAfter, Minamata]
-
A.
Minamata
Minamata is a powerful photo-essay and book by W. Eugene Smith documenting the devastating effects of industrial mercury poisoning on a Japanese fishing community.
-
B.
Minamata
chosen
Minamata is a coastal Japanese city historically known for the devastating industrial mercury poisoning disaster that led to the identification of Minamata disease.
-
C.
Daigo
Daigo was the era name (nengō) in Japanese history corresponding to the reign of Emperor Daigo in the early 10th century.
-
D.
Toyokawa
Toyokawa is a city in Aichi Prefecture, Japan, known for its historic Toyokawa Inari temple and manufacturing industries.
-
E.
Sendai
Sendai is the largest city in Japan’s Tōhoku region, known for its lush greenery, historic sites, and status as a major economic and cultural center in northeastern Honshu.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aa9b46cc8190b19f9f0cc45bf322 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79af7b72c8190a19dbcbb3a69fb5b |
completed | April 9, 2026, 12:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e441d376f8819080effd5bf29c6bc1 |
completed | April 19, 2026, 2:45 a.m. |
Created at: April 8, 2026, 9:28 p.m.