Triple
T31639388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | capture of Spanish frigate Gamo |
E807403
|
entity |
| Predicate | SpanishShipArmamentApproximate |
P172041
|
FINISHED |
| Object | 32 guns |
—
|
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: 32 guns | Statement: [capture of Spanish frigate Gamo, SpanishShipArmamentApproximate, 32 guns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: SpanishShipArmamentApproximate Context triple: [capture of Spanish frigate Gamo, SpanishShipArmamentApproximate, 32 guns]
-
A.
approximateEnglishShips
Indicates that one entity is an estimated or approximate count of English ships associated with another entity or context.
-
B.
SpanishFleetStrength
Indicates the relative size or power of a Spanish naval fleet in a given context or time.
-
C.
warshipTypeUsed
Indicates that a particular type or class of warship is employed or utilized in a given context, event, or operation.
-
D.
notableShip
Indicates that there is a notable or significant ship associated with the subject entity.
-
E.
BritishShipRate
Indicates that the subject is classified or rated according to the British naval ship rating system (e.g., first-rate, second-rate, etc.).
- 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_69f348d892948190915f8facacb9568c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a956e9b08190bf83547bba8e8147 |
completed | May 3, 2026, 1:48 a.m. |
| PD | Predicate disambiguation | batch_69f6a757c6e081908e37631e5d8d246b |
completed | May 3, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f6a8036ab481908019f2f071fa406e |
completed | May 3, 2026, 1:42 a.m. |
Created at: April 30, 2026, 10:48 p.m.