Triple
T21747339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NAB Coronado |
E536821
|
entity |
| Predicate | hasRunwayOrBeachAccess |
P145213
|
FINISHED |
| Object | amphibious training beaches |
—
|
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: amphibious training beaches | Statement: [NAB Coronado, hasRunwayOrBeachAccess, amphibious training beaches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRunwayOrBeachAccess Context triple: [NAB Coronado, hasRunwayOrBeachAccess, amphibious training beaches]
-
A.
hasRunwayAccessVia
Indicates that an entity has access to a runway by means of a specified connecting route, facility, or intermediary.
-
B.
hasRunwayAccessTo
Indicates that one location or facility is directly connected to another via a usable runway, allowing aircraft to move between them without leaving runway infrastructure.
-
C.
hasSeaAccess
Indicates that an entity has direct access to the sea, typically via a coastline, port, or navigable waterway connected to the sea.
-
D.
hasBeach
Indicates that one entity possesses, includes, or is characterized by a beach as part of its features or environment.
-
E.
hasBeachSurface
Indicates that one entity has a beach characterized by a particular type of surface.
- F. None of above. chosen
Provenance (4 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_69e0c46df5448190b4322127ffc4c690 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01a771b908190886cade242e263e4 |
completed | April 28, 2026, 2:24 a.m. |
| PD | Predicate disambiguation | batch_69e6969c16fc8190b5126c169317d85d |
completed | April 20, 2026, 9:11 p.m. |
| PDg | Predicate description generation | batch_69e69f3ed4408190a4a78410bf660c44 |
completed | April 20, 2026, 9:48 p.m. |
Created at: April 16, 2026, 6:49 p.m.