Triple
T12791768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Miami River terminals |
E305782
|
entity |
| Predicate | typicalVoyageRange |
P24304
|
FINISHED |
| Object | short to medium haul |
—
|
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: short to medium haul | Statement: [Port of Miami River terminals, typicalVoyageRange, short to medium haul]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalVoyageRange Context triple: [Port of Miami River terminals, typicalVoyageRange, short to medium haul]
-
A.
maximumRangeNauticalMiles
Indicates the greatest distance, measured in nautical miles, that something can travel or operate under specified conditions.
-
B.
ferryRange
Indicates the maximum distance an aircraft can fly without payload or passengers, typically with full fuel, under specified conditions.
-
C.
aircraftRangeCategory
Indicates the classification of an aircraft based on the distance it is capable of flying on a typical mission or with standard fuel capacity.
-
D.
typicalTrackLengthRange
Indicates the usual minimum and maximum lengths that a track associated with something tends to fall between.
-
E.
voyageType
chosen
Indicates the specific category or nature of a journey or trip that an entity undertakes.
- 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_69d7bdf366888190a8cccb982606889c |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e6b55248190ab938e69eb263612 |
completed | April 10, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69d9640ba0688190973e4e7ec8d4a8e0 |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:30 p.m.