Triple
T13594204
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Type 30 bayonet |
E324771
|
entity |
| Predicate | arsenalProducedAt |
P110213
|
FINISHED |
| Object | Jinsen Arsenal |
E327532
|
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: Jinsen Arsenal | Statement: [Type 30 bayonet, arsenalProducedAt, Jinsen Arsenal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jinsen Arsenal Context triple: [Type 30 bayonet, arsenalProducedAt, Jinsen Arsenal]
-
A.
Jinsen Arsenal
chosen
Jinsen Arsenal was an Imperial Japanese military arms factory in Korea that manufactured weapons such as the Type 99 rifle during World War II.
-
B.
Armas
Armas is a Finnish given name, notably used as one of the names of the renowned Finnish poet Eino Leino.
-
C.
Fisher Tank Arsenal
Fisher Tank Arsenal was an American defense manufacturing facility known for producing armored vehicles such as the M46 Patton tank.
-
D.
Tokyo Arsenal
Tokyo Arsenal was a major Imperial Japanese government weapons factory known for manufacturing military firearms and equipment in the late 19th and early 20th centuries.
-
E.
Fussilat
Fussilat is the 41st chapter of the Qur’an, known for its detailed exposition of divine revelation, signs in creation, and the consequences of accepting or rejecting the message.
- 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbe99ddc08190a8d79107c8e176fa |
completed | April 12, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f76bc762a08190b5d29cef9923da84 |
completed | May 3, 2026, 3:37 p.m. |
Created at: April 9, 2026, 9:49 p.m.