Triple
T24383558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maine-class armored cruiser |
E614677
|
entity |
| Predicate | hasShipTypeCharacteristic |
P155997
|
FINISHED |
| Object | mixed offensive and defensive capabilities |
—
|
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: mixed offensive and defensive capabilities | Statement: [Maine-class armored cruiser, hasShipTypeCharacteristic, mixed offensive and defensive capabilities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasShipTypeCharacteristic Context triple: [Maine-class armored cruiser, hasShipTypeCharacteristic, mixed offensive and defensive capabilities]
-
A.
hasShipCategory
Indicates that an entity is associated with or classified under a particular category or type of ship.
-
B.
hasShipRole
Indicates that an entity holds or is assigned a specific role or function on a ship.
-
C.
hasVesselType
Indicates that an entity is associated with or classified by a specific type of vessel (e.g., ship, boat, or container).
-
D.
hasShipyardType
Indicates the specific category or classification of shipyard associated with an entity.
-
E.
hasShippingLaneType
Indicates the specific category or type of shipping lane associated with a given route or area.
- 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_69e2d7e362e481909e32fe4ef8269d4f |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f294533b3881908c228a021c254006 |
completed | April 29, 2026, 11:29 p.m. |
| PD | Predicate disambiguation | batch_69f287c4a2b48190b80fb7a3c0e9b018 |
completed | April 29, 2026, 10:35 p.m. |
| PDg | Predicate description generation | batch_69f28f4d978c81908310c01def2514cc |
completed | April 29, 2026, 11:07 p.m. |
Created at: April 18, 2026, 2:03 a.m.