Triple
T118177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nice |
E2387
|
entity |
| Predicate | airportRankInFranceByTraffic |
P7613
|
FINISHED |
| Object | one of the busiest |
—
|
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: one of the busiest | Statement: [Nice, airportRankInFranceByTraffic, one of the busiest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airportRankInFranceByTraffic Context triple: [Nice, airportRankInFranceByTraffic, one of the busiest]
-
A.
peakPassengerTrafficRank
Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
-
B.
peakFreightTrafficRank
Indicates the relative ranking position of an entity based on the highest level of freight traffic it experiences or handles compared to others.
-
C.
airTraffic
Indicates the movement and flow of aircraft through airspace, including their routes, density, and interactions while in flight.
-
D.
largestAirport
Indicates that one airport is the largest (typically by area, traffic, or capacity) among a specified set or within a given region.
-
E.
airlineHub
Indicates that a particular location (typically an airport or city) serves as a central hub or primary operational base for an airline.
- 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_69a2506c5428819085c28a8884790e29 |
completed | Feb. 28, 2026, 2:18 a.m. |
| NER | Named-entity recognition | batch_69a258e0b11c8190b7b5cf3c354c47ce |
completed | Feb. 28, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69a25646d5088190a057989c32da3a90 |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a258de46888190835db2b21a093eaa |
completed | Feb. 28, 2026, 2:54 a.m. |
Created at: Feb. 28, 2026, 2:24 a.m.