Triple
T764931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nice Côte d’Azur Airport |
E16153
|
entity |
| Predicate | hasPassengerTrafficRankInFrance |
P7613
|
FINISHED |
| Object | third-busiest airport |
—
|
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: third-busiest airport | Statement: [Nice Côte d’Azur Airport, hasPassengerTrafficRankInFrance, third-busiest airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerTrafficRankInFrance Context triple: [Nice Côte d’Azur Airport, hasPassengerTrafficRankInFrance, third-busiest airport]
-
A.
cargoTrafficRankInFrance
Indicates the ranking position of an entity based on the volume of cargo traffic it handles within France.
-
B.
airportRankInFranceByTraffic
chosen
Indicates the relative position of an airport in France when airports are ordered by the volume of passenger or cargo traffic they handle.
-
C.
passengerTrafficRankInEurope
Indicates the relative position of an entity in Europe based on the volume of passenger traffic it handles.
-
D.
peakPassengerTrafficRank
Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
-
E.
populationRankInFrance
Indicates the relative position of an entity in an ordered list based on its population size within France.
- 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_69a493684ee48190bd43b7c78da4aec8 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a69dfeb08190b54a476cfa66e6d6 |
completed | March 1, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69a4a506106081909ef97a679ff00a5a |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.