Triple
T16848486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hound Point Terminal |
E409606
|
entity |
| Predicate | hasTankerMooringType |
P35993
|
FINISHED |
| Object | single point mooring |
—
|
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: single point mooring | Statement: [Hound Point Terminal, hasTankerMooringType, single point mooring]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTankerMooringType Context triple: [Hound Point Terminal, hasTankerMooringType, single point mooring]
-
A.
hasTankerClass
Indicates that an entity is associated with, or belongs to, a particular class or category of tanker.
-
B.
hasTankType
Indicates that an entity is associated with, or classified by, a specific type or category of tank.
-
C.
berthType
chosen
Indicates the specific kind or category of berth associated with an entity, such as the type of sleeping or docking space provided.
-
D.
harborType
Indicates the specific kind or classification of a harbor associated with an entity.
-
E.
hasVesselType
Indicates that an entity is associated with or classified by a specific type of vessel (e.g., ship, boat, or container).
- 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b376bac48190ae09f29a28c55f8c |
completed | April 18, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69e32b87b4248190aaddb05e88452356 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:24 a.m.