Triple
T13594210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Type 30 bayonet |
E324771
|
entity |
| Predicate | militaryDesignation |
P7137
|
FINISHED |
| Object |
Type 30
Type 30 is a Japanese military bayonet model historically issued with Arisaka rifles in the early 20th century.
|
E1049530
|
NE FINISHED |
How this triple was built (4 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: Type 30 | Statement: [Type 30 bayonet, militaryDesignation, Type 30]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Type 30 Context triple: [Type 30 bayonet, militaryDesignation, Type 30]
-
A.
S30
S30 is the pennant number of HMS Vigilant, a Royal Navy Vanguard-class nuclear-powered ballistic missile submarine.
-
B.
R30
R30 is a regional commuter rail line within the Rodalies de Catalunya network serving the Barcelona metropolitan area and surrounding regions.
-
C.
MIB30
MIB30 was a former benchmark stock market index of the Italian equity market, comprising 30 of the most liquid and capitalized companies listed on the Borsa Italiana.
-
D.
M30
M30 is a globular star cluster in the constellation Capricornus, notable for its dense core and great age.
-
E.
The 305
The 305 is a nickname commonly used to refer to Miami, Florida, derived from its original area code.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Type 30 Triple: [Type 30 bayonet, militaryDesignation, Type 30]
Generated description
Type 30 is a Japanese military bayonet model historically issued with Arisaka rifles in the early 20th century.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Type 30 Target entity description: Type 30 is a Japanese military bayonet model historically issued with Arisaka rifles in the early 20th century.
-
A.
S30
S30 is the pennant number of HMS Vigilant, a Royal Navy Vanguard-class nuclear-powered ballistic missile submarine.
-
B.
R30
R30 is a regional commuter rail line within the Rodalies de Catalunya network serving the Barcelona metropolitan area and surrounding regions.
-
C.
MIB30
MIB30 was a former benchmark stock market index of the Italian equity market, comprising 30 of the most liquid and capitalized companies listed on the Borsa Italiana.
-
D.
M30
M30 is a globular star cluster in the constellation Capricornus, notable for its dense core and great age.
-
E.
The 305
The 305 is a nickname commonly used to refer to Miami, Florida, derived from its original area code.
- F. None of above. chosen
Provenance (5 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_69dbb057f1c881909a3bb77c659a724a |
completed | April 12, 2026, 2:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f76bc762a08190b5d29cef9923da84 |
completed | May 3, 2026, 3:37 p.m. |
| NEDg | Description generation | batch_69f77643b0348190962bf23a9857edbe |
completed | May 3, 2026, 4:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f77923fd1481908af251a1dcbcf441 |
completed | May 3, 2026, 4:34 p.m. |
Created at: April 9, 2026, 9:49 p.m.