Triple
T12934918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cape St Vincent 1797 |
E309482
|
entity |
| Predicate | fleetSizeSpanish |
P1848
|
FINISHED |
| Object | 27 ships of the line |
—
|
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: 27 ships of the line | Statement: [Cape St Vincent 1797, fleetSizeSpanish, 27 ships of the line]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fleetSizeSpanish Context triple: [Cape St Vincent 1797, fleetSizeSpanish, 27 ships of the line]
-
A.
SpanishFleetStrength
Indicates the relative size or power of a Spanish naval fleet in a given context or time.
-
B.
fleetSize
chosen
Indicates the total number of vehicles, vessels, or units that collectively make up a fleet associated with an entity.
-
C.
SpanishSquadron
Indicates a relationship where an entity is identified as, or associated with, a naval or military squadron belonging to Spain.
-
D.
commanderForSpain
Indicates that a person serves or has served as a military commander on behalf of Spain.
-
E.
numberOfShips
Indicates the quantity of ships associated with a given entity or situation.
- 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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97db69f548190a1a693bc0d6c191a |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 5:42 p.m.