Triple
T8555306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port Dickson beaches |
E202548
|
entity |
| Predicate | typicalTravelTimeFromKualaLumpurByCar |
P46906
|
FINISHED |
| Object | about 1–2 hours |
—
|
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: about 1–2 hours | Statement: [Port Dickson beaches, typicalTravelTimeFromKualaLumpurByCar, about 1–2 hours]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTravelTimeFromKualaLumpurByCar Context triple: [Port Dickson beaches, typicalTravelTimeFromKualaLumpurByCar, about 1–2 hours]
-
A.
distanceFromKualaLumpur
Indicates the spatial distance between a given location or entity and Kuala Lumpur.
-
B.
travelTimeTypical
chosen
Indicates the usual or expected amount of time it takes to travel between two locations under normal conditions.
-
C.
travelTimeCategory
Indicates the qualitative classification of how long a given travel or trip duration is (e.g., short, medium, long).
-
D.
distanceToKota
Indicates the measured distance between a given entity and the location referred to as Kota.
-
E.
travelTimeFromBogotáByCar
Indicates the amount of time it takes to travel by car from Bogotá to a specified destination.
- 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_69ca832610e08190b3b6c6cd2c250255 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe88a936c8190a0234bf7da2ff55a |
completed | March 31, 2026, 3:30 p.m. |
| PD | Predicate disambiguation | batch_69cbd1160fcc8190aa380a73610af731 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:19 p.m.