Triple
T1092769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zurich Hauptbahnhof |
E24201
|
entity |
| Predicate | hasDailyPassengerVolume |
P17463
|
FINISHED |
| Object | hundreds of thousands of passengers per day |
—
|
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: hundreds of thousands of passengers per day | Statement: [Zurich Hauptbahnhof, hasDailyPassengerVolume, hundreds of thousands of passengers per day]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDailyPassengerVolume Context triple: [Zurich Hauptbahnhof, hasDailyPassengerVolume, hundreds of thousands of passengers per day]
-
A.
hasApproxAnnualPassengerUsageRank
Indicates the approximate position or ranking of an entity based on its annual passenger usage compared to similar entities.
-
B.
passengerTraffic
Indicates the flow or volume of passengers moving through or using a particular transport service, route, or facility.
-
C.
annualRidership
Indicates the total number of passengers who use a transportation service over the course of one year.
-
D.
peakDailyTrains
Indicates the maximum number of trains operating per day on a given route, line, or segment during its busiest period.
-
E.
dailyRidershipCategory
chosen
Indicates the classification of an entity based on the typical number of riders it serves per day.
- 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_69a4940542308190ac2a0b1f730b7cfc |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b99bd06c8190bce1d77b0337b07c |
completed | March 1, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69a4b743175481908f3967e589717c55 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.