Triple
T9971004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hakata ramen |
E196202
|
entity |
| Predicate | associatedWithRegion |
P285
|
FINISHED |
| Object | Hakata |
E116728
|
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: Hakata | Statement: [Hakata ramen, associatedWithRegion, Hakata]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hakata Context triple: [Hakata ramen, associatedWithRegion, Hakata]
-
A.
Hakata–Shin-Yatsushiro
Hakata–Shin-Yatsushiro is a key segment of Japan’s Kyushu Shinkansen high-speed rail line, connecting the major city of Fukuoka (Hakata) with Shin-Yatsushiro in Kumamoto Prefecture.
-
B.
Karatsu
Karatsu is a coastal city in Saga Prefecture, Japan, known for its historic castle, traditional Karatsu ware pottery, and the annual Karatsu Kunchi festival.
-
C.
Ōsaki
Ōsaki is a major commercial and business district in Tokyo known for its high-rise office complexes and convenient rail connections.
-
D.
Hakata Port
chosen
Hakata Port is a major international seaport in Fukuoka, Japan, serving as a key gateway for passenger ferries and cargo between Kyushu, the rest of Japan, and East Asia.
-
E.
Toyokawa
Toyokawa is a city in Aichi Prefecture, Japan, known for its historic Toyokawa Inari temple and manufacturing industries.
- 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_69ca82eea2b88190a0e511d21a31f386 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb7b96b1c8190b9d3c1171346615a |
completed | April 2, 2026, 12:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d257c9f6cc81908256dc1e8d6c3fea |
completed | April 5, 2026, 12:38 p.m. |
Created at: March 30, 2026, 8:48 p.m.