Triple
T7801393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U-Tapao–Rayong–Pattaya International Airport |
E180440
|
entity |
| Predicate | distanceFromRayong |
P79083
|
FINISHED |
| Object | approximately 40 kilometres |
—
|
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: approximately 40 kilometres | Statement: [U-Tapao–Rayong–Pattaya International Airport, distanceFromRayong, approximately 40 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromRayong Context triple: [U-Tapao–Rayong–Pattaya International Airport, distanceFromRayong, approximately 40 kilometres]
-
A.
distanceFromBangkok
Indicates the spatial distance between a given location and the city of Bangkok.
-
B.
distanceFromPattaya
Indicates the spatial distance between a given place or object and the location of Pattaya.
-
C.
distanceFromPattayaCityCentre
Indicates the measured distance between a given location and the central area of Pattaya City.
-
D.
distanceFromKualaLumpur
Indicates the spatial distance between a given location or entity and Kuala Lumpur.
-
E.
distanceFromManila
Indicates the measured spatial distance between a given entity’s location and the city of Manila.
- 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_69ca827e50cc8190a92a733577184938 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78a6d88819093f83528fe88b182 |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae9111b2481909684a2d4aa4831c2 |
completed | March 30, 2026, 9:20 p.m. |
| PDg | Predicate description generation | batch_69caf7855a3c81908b9318f7186fc0c0 |
completed | March 30, 2026, 10:21 p.m. |
Created at: March 30, 2026, 4:33 p.m.