Triple
T34927704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Transbay BART network |
E1007336
|
entity |
| Predicate | hasDailyRidershipRole |
P17463
|
FINISHED |
| Object | highest-volume segment of BART |
—
|
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: highest-volume segment of BART | Statement: [Transbay BART network, hasDailyRidershipRole, highest-volume segment of BART]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDailyRidershipRole Context triple: [Transbay BART network, hasDailyRidershipRole, highest-volume segment of BART]
-
A.
annualRidership
Indicates the total number of passengers who use a transportation service over the course of one year.
-
B.
dailyRidershipCategory
chosen
Indicates the classification of an entity based on the typical number of riders it serves per day.
-
C.
hasDailyPassengerTraffic
Indicates the number of passengers that regularly use or pass through something (such as a station or route) each day.
-
D.
dailyRidership
Indicates the typical number of people who use or ride a given transportation service each day.
-
E.
hasSeasonalRidershipPeak
Indicates that the ridership of an entity (such as a service or route) reaches its highest levels during specific seasons or times of the year.
- 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_69f76dc3d83881909d5c3c14455cfa2c |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78710282c81909146dc0be91e983f |
completed | May 3, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f784162134819098413482ef52042f |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4 p.m.