Triple
T239233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toronto Transit Commission |
E4890
|
entity |
| Predicate | annualRidershipApprox |
P882
|
FINISHED |
| Object | over 500 million trips in peak years |
—
|
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 500 million trips in peak years | Statement: [Toronto Transit Commission, annualRidershipApprox, over 500 million trips in peak years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: annualRidershipApprox Context triple: [Toronto Transit Commission, annualRidershipApprox, over 500 million trips in peak years]
-
A.
dailyRidershipPeak
Indicates that the relationship specifies the highest number of riders or users recorded for a service or system within a single day.
-
B.
hasLightRailSystem
Indicates that a place possesses and operates a light rail transit system.
-
C.
passengerTrafficRankUS
Indicates the relative ranking of a location or facility within the United States based on the volume of passenger traffic it handles.
-
D.
passengersCountApproximate
chosen
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
E.
fareSystem
Indicates a relationship where a system is used to determine, collect, or manage fares or payments for transportation or similar services.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25dacf60c8190a5c3ef455b9a8b20 |
completed | Feb. 28, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69a25b5f27208190ae13f34037fe582b |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.