Triple
T13558225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Money Island |
E323834
|
entity |
| Predicate | hasNearbySeaLanes |
P10083
|
FINISHED |
| Object | major international shipping routes in South China Sea |
—
|
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: major international shipping routes in South China Sea | Statement: [Money Island, hasNearbySeaLanes, major international shipping routes in South China Sea]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbySeaLanes Context triple: [Money Island, hasNearbySeaLanes, major international shipping routes in South China Sea]
-
A.
nearbySeaLane
Indicates that one entity is located close to a sea lane used for maritime navigation or shipping.
-
B.
hasMajorSeaLane
chosen
Indicates that a significant maritime shipping or navigation route passes through, borders, or is closely associated with the referenced entity.
-
C.
hasNearbyHarbor
Indicates that one location has a harbor situated close to it in geographic proximity.
-
D.
hasDestinationSea
Indicates that something is directed or travels toward a sea as its endpoint or target location.
-
E.
hasNearbyCoast
Indicates that one location is situated close to a coastline or seashore.
- 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_69d8076830b48190910a902bae5888e2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae13bec4819084c1770638c00ed9 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:47 p.m.