Triple
T259931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USS West Virginia (BB-48) |
E5518
|
entity |
| Predicate | standardDisplacement |
P10289
|
FINISHED |
| Object | 32500 long tons |
—
|
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: 32500 long tons | Statement: [USS West Virginia (BB-48), standardDisplacement, 32500 long tons]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: standardDisplacement Context triple: [USS West Virginia (BB-48), standardDisplacement, 32500 long tons]
-
A.
shipClass
Indicates the classification or type category to which a particular ship belongs.
-
B.
hullNumber
Indicates the unique identifying number assigned to the hull of a ship or vessel.
-
C.
displacementFullLoad
Indicates the total volume of water displaced by a vessel when it is fully loaded to its maximum operational capacity.
-
D.
originalShipType
Indicates the type or category of ship that an entity was originally classified or built as.
-
E.
assistedShip
Indicates that one entity helped or supported a ship in performing an operation, task, or journey.
- F. None of above. chosen
Provenance (4 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25f921c2881908821ca2c03815eae |
completed | Feb. 28, 2026, 3:22 a.m. |
| PD | Predicate disambiguation | batch_69a25b6b3ea88190bbd858999e42efae |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25f9163f881909232f8aea502cf80 |
completed | Feb. 28, 2026, 3:22 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.