Triple
T16184974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orange Line Metro Train Lahore |
E392776
|
entity |
| Predicate | dailyRidershipCapacity |
P10158
|
FINISHED |
| Object | over 250,000 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 250,000 passengers | Statement: [Orange Line Metro Train Lahore, dailyRidershipCapacity, over 250,000 passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dailyRidershipCapacity Context triple: [Orange Line Metro Train Lahore, dailyRidershipCapacity, over 250,000 passengers]
-
A.
dailyRidership
chosen
Indicates the typical number of people who use or ride a given transportation service each day.
-
B.
annualRidership
Indicates the total number of passengers who use a transportation service over the course of one year.
-
C.
dailyRidershipPeak
Indicates that the relationship specifies the highest number of riders or users recorded for a service or system within a single day.
-
D.
dailyRidershipCategory
Indicates the classification of an entity based on the typical number of riders it serves per 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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e22060dcf88190b7c662946a5f0191 |
completed | April 17, 2026, 11:58 a.m. |
| PD | Predicate disambiguation | batch_69e219d642708190ba31a90dce76a210 |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:02 a.m.