Triple
T6235984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Gravelines (1588) |
E139478
|
entity |
| Predicate | SpanishLosses |
P28546
|
FINISHED |
| Object | multiple ships sunk or disabled |
—
|
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: multiple ships sunk or disabled | Statement: [Battle of Gravelines (1588), SpanishLosses, multiple ships sunk or disabled]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: SpanishLosses Context triple: [Battle of Gravelines (1588), SpanishLosses, multiple ships sunk or disabled]
-
A.
causedTerritorialLossesTo
Indicates that one entity was responsible for another entity losing control over some portion of its territory.
-
B.
effectOnSpain
chosen
Indicates a relationship where one entity produces an influence, change, or consequence specifically affecting Spain.
-
C.
obligationsOfSpain
Indicates that certain duties, responsibilities, or commitments are borne by Spain in a given context.
-
D.
SpanishPapalCasualties
Indicates casualties that occurred among Spanish and Papal forces as a result of a shared conflict or military engagement.
-
E.
SpanishSquadron
Indicates a relationship where an entity is identified as, or associated with, a naval or military squadron belonging to Spain.
- 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_69c008b0e7ac8190808a59573ee646f3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062f236608190997e77b41095883f |
completed | March 22, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69c05601de6481909d0880048fd7b49a |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:23 p.m.