Triple
T3484629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Torino Porta Nuova |
E73576
|
entity |
| Predicate | passengerTrafficRankInItaly |
P25678
|
FINISHED |
| Object | one of the busiest railway stations in Italy |
—
|
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 Italy | Statement: [Torino Porta Nuova, passengerTrafficRankInItaly, one of the busiest railway stations in Italy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerTrafficRankInItaly Context triple: [Torino Porta Nuova, passengerTrafficRankInItaly, one of the busiest railway stations in Italy]
-
A.
passengerTrafficRankInEurope
Indicates the relative position of an entity in Europe based on the volume of passenger traffic it handles.
-
B.
otherMilanAirports
Indicates that the related entity is an airport in Milan other than the primary or main Milan airport under consideration.
-
C.
passengerTrafficRankingWorld
Indicates the relative position of an entity in a global ranking based on the volume of passenger traffic it handles.
-
D.
cargoTrafficRankInEurope
Indicates the relative position of an entity in terms of cargo traffic volume compared to other entities within Europe.
-
E.
hasPassengerTrafficRank
chosen
Indicates the relative position or ranking of an entity based on the volume of passenger traffic it handles compared to others.
- 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_69ad85b3c9b08190857cae74c7f36da9 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb795db88190805b26d9774fdb73 |
completed | March 8, 2026, 6:10 p.m. |
| PD | Predicate disambiguation | batch_69adae0935ac8190bfa8a8bd3dcd3301 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:17 p.m.