Triple
T32392042
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Costa Teguise |
E827696
|
entity |
| Predicate | airportServing |
P4363
|
FINISHED |
| Object | César Manrique-Lanzarote 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: César Manrique-Lanzarote Airport | Statement: [Costa Teguise, airportServing, César Manrique-Lanzarote Airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airportServing Context triple: [Costa Teguise, airportServing, César Manrique-Lanzarote Airport]
-
A.
airportServed
chosen
Indicates that a particular airport provides service to, or is used for air travel to and from, a given location or area.
-
B.
airportServesAs
Indicates that an airport functions in a particular role or capacity (such as primary, secondary, or hub) for a specified area, organization, or service.
-
C.
airportUse
Indicates that an airport is used or utilized by a particular entity, such as an airline, organization, or service.
-
D.
associatedAirportServes
Indicates that a given airport provides service to, or is used by, the associated entity (such as a city, region, or facility).
-
E.
airportStation
Indicates a location functions as an airport facility where air transport operations occur.
- 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_69f6f8164698819090c1b471f1caa4c6 |
completed | May 3, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69f6f6619404819084662aef1238261c |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 12:52 a.m.