Triple
T3034243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faro Airport |
E82968
|
entity |
| Predicate | servesSeasonalTraffic |
P45121
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Faro Airport, servesSeasonalTraffic, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesSeasonalTraffic Context triple: [Faro Airport, servesSeasonalTraffic, yes]
-
A.
servesGovernmentTraffic
Indicates that an entity provides services or functionality specifically for government-related network or data traffic.
-
B.
hasTrafficPattern
Indicates that there is a characteristic or recurring flow of traffic associated with an entity, such as its typical volume, direction, or timing of movement.
-
C.
supportsTraffic
Indicates that one entity is capable of handling, carrying, or accommodating the flow or volume of traffic associated with another entity.
-
D.
hasHeavyPassengerTraffic
Indicates that an entity experiences a high volume of passenger movement or usage over a given period.
-
E.
servesRidersTraveling
Indicates that a service or entity provides transportation-related service or support to riders who are currently traveling or in transit.
- 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_69ad8b21a62881908ec5dd4fba4a187c |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9b2a40b48190bfa7cdbb0fbd87f8 |
completed | March 8, 2026, 3:52 p.m. |
| PD | Predicate disambiguation | batch_69ad961e2a408190afb1759132701305 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97ba55dc8190b6dddddfb751cf64 |
completed | March 8, 2026, 3:37 p.m. |
Created at: March 8, 2026, 3:01 p.m.