Triple
T10189530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chambéry Airport |
E237995
|
entity |
| Predicate | focusTraffic |
P80470
|
FINISHED |
| Object | winter ski tourism |
—
|
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: winter ski tourism | Statement: [Chambéry Airport, focusTraffic, winter ski tourism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusTraffic Context triple: [Chambéry Airport, focusTraffic, winter ski tourism]
-
A.
trafficFocus
Indicates a focus of attention or priority given to a particular traffic element, flow, or direction within a transportation or network context.
-
B.
trafficType
Indicates the category or nature of traffic involved in a given interaction, flow, or connection (e.g., type of network, data, or transport traffic).
-
C.
touristTraffic
chosen
Indicates the level, flow, or intensity of tourists visiting or moving through a particular place or area.
-
D.
traffics
Indicates engaging in the buying, selling, or illicit trading of someone or something, typically as part of an ongoing commercial or criminal operation.
-
E.
annualTraffic
Indicates the typical amount or volume of traffic associated with something over the course of a year.
- 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cded7d6fdc81908052866495b6574f |
completed | April 2, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69cd7c8477648190bc55c56aeec507d3 |
completed | April 1, 2026, 8:13 p.m. |
Created at: March 30, 2026, 9:12 p.m.