Triple
T2809690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nevada-class battleship |
E54136
|
entity |
| Predicate | gunArrangementInnovation |
P20686
|
FINISHED |
| Object | first U.S. battleship class with triple turrets |
—
|
LITERAL 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: first U.S. battleship class with triple turrets | Statement: [Nevada-class battleship, gunArrangementInnovation, first U.S. battleship class with triple turrets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gunArrangementInnovation Context triple: [Nevada-class battleship, gunArrangementInnovation, first U.S. battleship class with triple turrets]
-
A.
militaryInnovation
chosen
Indicates the introduction or development of new methods, technologies, or strategies within a military context.
-
B.
gunType
Indicates the specific category or kind of gun associated with an entity.
-
C.
armedVariants
Indicates that one entity is a version or model of another that is equipped with weapons or enhanced armaments.
-
D.
weaponDesigner
Indicates that one entity is the creator or designer of a weapon used or associated with another entity.
-
E.
needleArrangement
Indicates how needles are positioned or organized relative to each other in a given context.
- F. None of above.
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_69ab49dcee188190b5c6eca9ae9e3469 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde3165b48190a43be5e6ad23deca |
completed | March 7, 2026, 8:13 a.m. |
| PD | Predicate disambiguation | batch_69abdd0740208190911dc9c9546a79ae |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:59 p.m.