Triple
T24277294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samila Beach |
E605442
|
entity |
| Predicate | closestAirport |
P22550
|
FINISHED |
| Object | Hat Yai International Airport |
—
|
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: Hat Yai International Airport | Statement: [Samila Beach, closestAirport, Hat Yai International Airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: closestAirport Context triple: [Samila Beach, closestAirport, Hat Yai International Airport]
-
A.
nearestAirport
chosen
Indicates that one airport is the closest in distance to a given location or entity compared to all other airports.
-
B.
nearestLargerAirport
Indicates that one airport is the closest geographically among all airports that are larger (e.g., by traffic or capacity) than a given reference airport.
-
C.
nearestAirportRegion
Indicates that a region is the closest airport-served area to a given location or entity.
-
D.
nearbyAirportRelationship
Indicates that one location has an airport situated close enough to serve it conveniently, establishing a nearby-airport relationship between the two.
-
E.
nearestCityTo
Indicates that one city is the closest in distance to a given location or entity compared to all other cities.
- 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_69e2954707dc8190915551eb114cfff6 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f28d60a454819093b46556966640ab |
completed | April 29, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69f1c457a2908190993824395b3c365d |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 18, 2026, 12:07 a.m.