Triple
T13410413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Lakes shipwrecks |
E320070
|
entity |
| Predicate | hasCauseOfLoss |
P708
|
FINISHED |
| Object | storms |
—
|
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: storms | Statement: [Great Lakes shipwrecks, hasCauseOfLoss, storms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCauseOfLoss Context triple: [Great Lakes shipwrecks, hasCauseOfLoss, storms]
-
A.
causedLossOf
Indicates that one entity brought about or was responsible for another entity experiencing a loss.
-
B.
hasCauseOfDestruction
Indicates that one entity is the cause or agent responsible for the destruction or damage of another entity.
-
C.
eligibleCause
Indicates that one entity qualifies as a valid or acceptable cause or reason for another entity or outcome.
-
D.
causedAccident
Indicates that one entity is responsible for bringing about or initiating an accident involving another entity or situation.
-
E.
hasCause
chosen
Indicates that one entity is the reason for, or brings about, 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaeb3facc819088c1af3b59237e7a |
completed | April 12, 2026, 2:39 p.m. |
| PD | Predicate disambiguation | batch_69d9a0355de48190bb3fb96912e20df3 |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:35 p.m.