Triple
T3317206
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SMS Derfflinger |
E69709
|
entity |
| Predicate | mainGunsArrangement |
P27374
|
FINISHED |
| Object | four twin 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: four twin turrets | Statement: [SMS Derfflinger, mainGunsArrangement, four twin turrets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainGunsArrangement Context triple: [SMS Derfflinger, mainGunsArrangement, four twin turrets]
-
A.
primaryArmament
Indicates the main weapon or principal offensive system that an entity (such as a vehicle, vessel, or platform) is equipped with or uses.
-
B.
numberOfMainBatteryGuns
Indicates the quantity of primary (main) battery guns that an entity, typically a warship or similar platform, is equipped with.
-
C.
numberOfGuns
Indicates the quantity of guns associated with a given entity or situation.
-
D.
gunType
Indicates the specific category or kind of gun associated with an entity.
-
E.
gunTurretConfiguration
chosen
Indicates the specific arrangement, placement, and setup of gun turrets in a system or structure.
- 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_69ad85a0bb048190a5458d2738012d61 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb11230b881908f5b554323729cc5 |
completed | March 8, 2026, 5:25 p.m. |
| PD | Predicate disambiguation | batch_69ada4282730819092aa39c5f9269df0 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:11 p.m.