Triple
T4478932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King Island |
E100078
|
entity |
| Predicate | hasShipwreckHistory |
P49889
|
FINISHED |
| Object | numerous shipwrecks around coastline |
—
|
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: numerous shipwrecks around coastline | Statement: [King Island, hasShipwreckHistory, numerous shipwrecks around coastline]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasShipwreckHistory Context triple: [King Island, hasShipwreckHistory, numerous shipwrecks around coastline]
-
A.
hasShipwrecks
chosen
Indicates that one entity contains, includes, or is associated with shipwrecks located within it or under its control.
-
B.
numberOfShipwrecks
Indicates the quantity of shipwrecks associated with a given entity or context.
-
C.
shipwreckEvent
Indicates an event in which a ship is destroyed, stranded, or severely damaged, typically resulting in loss or abandonment at sea or near a shoreline.
-
D.
shipwreckUse
Indicates that an entity makes use of, interacts with, or derives benefit from a shipwreck.
-
E.
causeOfShipwreck
Indicates the factor, event, or condition that directly led to a shipwreck occurring.
- 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_69b34553cbe48190afa8ac1cac285b86 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35728ed508190ba0e882fa62d8848 |
completed | March 13, 2026, 12:15 a.m. |
| PD | Predicate disambiguation | batch_69b3563d63008190816e37027e761375 |
completed | March 13, 2026, 12:11 a.m. |
Created at: March 12, 2026, 11:35 p.m.