Triple
T525341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LFPG |
E10903
|
entity |
| Predicate | cargoTrafficRankInFrance |
P15147
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [LFPG, cargoTrafficRankInFrance, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cargoTrafficRankInFrance Context triple: [LFPG, cargoTrafficRankInFrance, 1]
-
A.
airportRankInFranceByTraffic
Indicates the relative position of an airport in France when airports are ordered by the volume of passenger or cargo traffic they handle.
-
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.
populationRankInFrance
Indicates the relative position of an entity in an ordered list based on its population size within France.
-
D.
annualTraffic
Indicates the typical amount or volume of traffic associated with something over the course of a year.
-
E.
passengerTrafficRankUS
Indicates the relative ranking of a location or facility within the United States based on the volume of passenger traffic it handles.
- 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_69a2e84b16c4819088d284c47c3a7968 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1b7f448819087e5e7f3b37d7142 |
completed | Feb. 28, 2026, 1:46 p.m. |
| PD | Predicate disambiguation | batch_69a2f0198ecc8190883849e5a8245963 |
completed | Feb. 28, 2026, 1:39 p.m. |
| PDg | Predicate description generation | batch_69a2f0dcff1881909c18e8c599c150a1 |
completed | Feb. 28, 2026, 1:42 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.