Triple
T7912709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puerto del Carmen |
E183736
|
entity |
| Predicate | distanceToAirport |
P79745
|
FINISHED |
| Object | approximately 10 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 10 kilometres | Statement: [Puerto del Carmen, distanceToAirport, approximately 10 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAirport Context triple: [Puerto del Carmen, distanceToAirport, approximately 10 kilometres]
-
A.
nearestAirport
Indicates that one airport is the closest in distance to a given location or entity compared to all other airports.
-
B.
distanceToFrankfurtAirport_km
Indicates the physical distance, measured in kilometers, between a given location and Frankfurt Airport.
-
C.
distanceFromLombokInternationalAirport
Indicates the measured distance between a given location and Lombok International Airport.
-
D.
travelTimeToAirport
Indicates the amount of time required to travel from a given location to an airport.
-
E.
distanceToOsloAirportGardermoen_km
Indicates the physical distance, measured in kilometers, between a given location and Oslo Airport Gardermoen.
- 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_69ca828dec0c81908b8f55a4dbbb53ff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a7383cc819084eab19799209d2e |
completed | March 31, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
| PDg | Predicate description generation | batch_69caf7882b048190baa333af9f698590 |
completed | March 30, 2026, 10:22 p.m. |
Created at: March 30, 2026, 5:04 p.m.