Triple
T1603486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shinjuku Station |
E34446
|
entity |
| Predicate | hasDailyPassengerTraffic |
P30663
|
FINISHED |
| Object | over 3 million passengers |
—
|
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: over 3 million passengers | Statement: [Shinjuku Station, hasDailyPassengerTraffic, over 3 million passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDailyPassengerTraffic Context triple: [Shinjuku Station, hasDailyPassengerTraffic, over 3 million passengers]
-
A.
passengerTraffic
Indicates the flow or volume of passengers moving through or using a particular transport service, route, or facility.
-
B.
hasAnnualPassengerTrafficOver
Indicates that the subject location or transport facility experiences an annual passenger volume exceeding a specified threshold.
-
C.
hasPassengerTrafficRank
Indicates the relative position or ranking of an entity based on the volume of passenger traffic it handles compared to others.
-
D.
hasApproxAnnualPassengerUsageRank
Indicates the approximate position or ranking of an entity based on its annual passenger usage compared to similar entities.
-
E.
peakPassengerTrafficRank
Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
- 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_69a885fea6a481909fe83ba6441f1774 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a95b02cd448190be8e3db9a5a7bac0 |
completed | March 5, 2026, 10:29 a.m. |
| PD | Predicate disambiguation | batch_69a907c1cad08190b9728dd557f39aa0 |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a95aada3f881909053363c01de8b57 |
completed | March 5, 2026, 10:29 a.m. |
Created at: March 4, 2026, 7:28 p.m.