Triple
T34811272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint Barthélemy Airport |
E1003503
|
entity |
| Predicate | proximityToBeach |
P90459
|
FINISHED |
| Object | St. Jean Beach |
—
|
NE NERFINISHED |
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: St. Jean Beach | Statement: [Saint Barthélemy Airport, proximityToBeach, St. Jean Beach]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proximityToBeach Context triple: [Saint Barthélemy Airport, proximityToBeach, St. Jean Beach]
-
A.
hasBeachNearby
Indicates that one location is situated close enough to another location to have convenient access to a beach.
-
B.
nearestMajorBeach
chosen
Indicates the relationship where a location is associated with the closest significant or well-known beach to it.
-
C.
distanceFromCoast
Indicates the measured spatial separation between a location and the nearest point on a coastline.
-
D.
hasBeach
Indicates that one entity possesses, includes, or is characterized by a beach as part of its features or environment.
-
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_69f76db600b88190989abdf08fce3b27 |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f77ab5883c8190b5d7b08f22472e0b |
completed | May 3, 2026, 4:41 p.m. |
| PD | Predicate disambiguation | batch_69f7795b1abc8190823664d1caa94649 |
completed | May 3, 2026, 4:35 p.m. |
Created at: May 3, 2026, 3:59 p.m.