Triple
T13762842
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kokura Arsenal |
E330657
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Kokura |
—
|
NE NERFINISHED |
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: Kokura | Statement: [Kokura Arsenal, locatedIn, Kokura]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kokura Context triple: [Kokura Arsenal, locatedIn, Kokura]
-
A.
Kokura
chosen
Kokura is a historic district in Kitakyushu, Japan, known for its castle, commercial center, and role as a former castle town and transport hub on Kyushu.
-
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.
Toyokawa
Toyokawa is a city in Aichi Prefecture, Japan, known for its historic Toyokawa Inari temple and manufacturing industries.
-
D.
Toyoake
Toyoake is a city in central Japan known for its residential communities and proximity to the Nagoya metropolitan area in Aichi Prefecture.
-
E.
Ichinoseki
Ichinoseki is a city in northeastern Japan known as a gateway to the scenic and historic sites of southern Iwate Prefecture.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02250f4881908d5193b3d5d25844 |
completed | April 14, 2026, 9 a.m. |
Created at: April 9, 2026, 10:10 p.m.