Triple
T18563213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rangaunu Harbour |
E453697
|
entity |
| Predicate | hasInletOrArm |
P23365
|
FINISHED |
| Object | Kaimaumau wetland margins |
—
|
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: Kaimaumau wetland margins | Statement: [Rangaunu Harbour, hasInletOrArm, Kaimaumau wetland margins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInletOrArm Context triple: [Rangaunu Harbour, hasInletOrArm, Kaimaumau wetland margins]
-
A.
hasInlet
chosen
Indicates that one entity serves as an inlet or entry point through which another entity receives a flow of material, energy, or fluid.
-
B.
hasArm
Indicates that one entity possesses or is equipped with an arm as a physical part or component of itself.
-
C.
hasOutlet
Indicates that one entity provides, contains, or is equipped with an outlet or point of access for another entity.
-
D.
hasPortOn
Indicates that one entity possesses or is located adjacent to a port situated on another specified geographic or infrastructural feature (such as a coast, river, or lake).
-
E.
hasArmCount
Indicates the number of arms that an entity possesses.
- 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_69d8d38974308190a9174430ef256b73 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53afc57448190abd90167d7e12a18 |
completed | April 19, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69e478c16e0c8190b03966aa23c395a6 |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:42 a.m.