Triple
T30966872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French invasion of Fishguard |
E788982
|
entity |
| Predicate | hasCausalFactor |
P708
|
FINISHED |
| Object | French Revolutionary Wars |
—
|
NE NERFINISHED |
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: French Revolutionary Wars | Statement: [French invasion of Fishguard, hasCausalFactor, French Revolutionary Wars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCausalFactor Context triple: [French invasion of Fishguard, hasCausalFactor, French Revolutionary Wars]
-
A.
hasCause
chosen
Indicates that one entity is the reason for, or brings about, the occurrence or existence of another entity or event.
-
B.
hasProposedCause
Indicates that one entity is suggested or hypothesized to be the cause or explanation for another entity or event.
-
C.
hasFactor
Indicates that one entity is a factor or divisor of another entity in a multiplicative relationship.
-
D.
hasCauseOfVariability
Indicates a relationship where one factor or condition is identified as the source or driver of variation observed in another.
-
E.
causeOf
Indicates that one entity brings about, produces, or is responsible for the occurrence or existence of another entity or event.
- 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_69f224c3a6b48190951add9b7b7f0271 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fe38be079c8190a240191ac0e73e3a |
completed | May 8, 2026, 7:25 p.m. |
| PD | Predicate disambiguation | batch_69fe350344508190930de2218156ca02 |
completed | May 8, 2026, 7:09 p.m. |
Created at: April 29, 2026, 8:54 p.m.