Triple
T6819236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JR Lines at Kanda |
E156853
|
entity |
| Predicate | passengerFlow |
P16273
|
FINISHED |
| Object | high daily ridership |
—
|
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: high daily ridership | Statement: [JR Lines at Kanda, passengerFlow, high daily ridership]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerFlow Context triple: [JR Lines at Kanda, passengerFlow, high daily ridership]
-
A.
passengerTraffic
chosen
Indicates the flow or volume of passengers moving through or using a particular transport service, route, or facility.
-
B.
hasPassengerTrafficFrom
Indicates that an entity receives or handles passenger traffic originating from another entity.
-
C.
hasDailyPassengerTraffic
Indicates the number of passengers that regularly use or pass through something (such as a station or route) each day.
-
D.
peakPassengerTrafficRank
Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
-
E.
hasPassengerTrafficRank
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_69c688298a288190af3f285d57f76bbe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d35781e88190a45d1386706d4422 |
completed | March 27, 2026, 6:58 p.m. |
| PD | Predicate disambiguation | batch_69c6d09bb4f881909bf20c188cb3e8e1 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:17 p.m.