Triple
T32392041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Costa Teguise |
E827696
|
entity |
| Predicate | distanceToAirport_km |
P79745
|
FINISHED |
| Object | approximately 15 |
—
|
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 15 | Statement: [Costa Teguise, distanceToAirport_km, approximately 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAirport_km Context triple: [Costa Teguise, distanceToAirport_km, approximately 15]
-
A.
distanceToAirport
chosen
Indicates the measured distance between a given location and the nearest or specified airport.
-
B.
distanceToMBBAirport_km
Indicates the distance, measured in kilometers, from a given location to the MBB airport.
-
C.
distanceToFrankfurtAirport_km
Indicates the physical distance, measured in kilometers, between a given location and Frankfurt Airport.
-
D.
distanceToOsloAirportGardermoen_km
Indicates the physical distance, measured in kilometers, between a given location and Oslo Airport Gardermoen.
-
E.
distanceToStockholmArlandaAirport
Indicates the measured distance between a given location or entity and Stockholm Arlanda Airport.
- 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_69f349184e7481909c6c54428cb9cf12 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a036b0755888190a68a673664f0ea46 |
completed | May 12, 2026, 6:01 p.m. |
| PD | Predicate disambiguation | batch_6a036228676c8190aac9c90f7d8cedf7 |
completed | May 12, 2026, 5:23 p.m. |
Created at: May 1, 2026, 12:52 a.m.