Triple
T14929124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris-Saint-Lazare station |
E372209
|
entity |
| Predicate | rankInParisByTraffic |
P116703
|
FINISHED |
| Object | one of the busiest railway stations in Paris |
—
|
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 railway stations in Paris | Statement: [Paris-Saint-Lazare station, rankInParisByTraffic, one of the busiest railway stations in Paris]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInParisByTraffic Context triple: [Paris-Saint-Lazare station, rankInParisByTraffic, one of the busiest railway stations in Paris]
-
A.
cargoTrafficRankInFrance
Indicates the ranking position of an entity based on the volume of cargo traffic it handles within France.
-
B.
cargoTrafficRank
Indicates the relative position of an entity in an ordered list based on the volume or intensity of its cargo traffic.
-
C.
cargoTrafficRankInEurope
Indicates the relative position of an entity in terms of cargo traffic volume compared to other entities within Europe.
-
D.
passengerTrafficRankingWorld
Indicates the relative position of an entity in a global ranking based on the volume of passenger traffic it handles.
-
E.
airportRankInFranceByTraffic
Indicates the relative position of an airport in France when airports are ordered by the volume of passenger or cargo traffic they handle.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded634e67881909daec9eaef188d09 |
completed | April 15, 2026, 12:05 a.m. |
| PD | Predicate disambiguation | batch_69de9a52ba988190a26e268b4ea083ea |
completed | April 14, 2026, 7:49 p.m. |
| PDg | Predicate description generation | batch_69deb1a4d8dc8190a4c0841c20f2875f |
completed | April 14, 2026, 9:29 p.m. |
Created at: April 10, 2026, 2:36 a.m.