Triple
T436068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hokkaido University |
E10012
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Sapporo |
E40366
|
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: Sapporo | Statement: [Hokkaido University, locatedIn, Sapporo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sapporo Context triple: [Hokkaido University, locatedIn, Sapporo]
-
A.
Sapporo
chosen
Sapporo is the capital and largest city of Japan’s northern Hokkaido prefecture, known for its annual snow festival, beer, and ski resorts.
-
B.
Niigata
Niigata is a major coastal city in north-central Japan known for its important seaport on the Sea of Japan, rice production, and sake brewing.
-
C.
Yokohama
Yokohama is Japan’s second-largest city and a major international port located just south of Tokyo.
-
D.
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.
-
E.
Nagoya
Nagoya is a major industrial and commercial city in central Japan, known as a manufacturing hub and the capital of Aichi Prefecture.
- 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_69a2e8465ef481909655c681b01e2986 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2ef0c97188190b62104cb639d4b60 |
completed | Feb. 28, 2026, 1:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acddeda4f8819080d37aea0fd4b092 |
completed | March 8, 2026, 2:24 a.m. |
Created at: Feb. 28, 2026, 1:11 p.m.