Triple
T28561526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Métro Line 3bis |
E722556
|
entity |
| Predicate | hasAverageRidershipLevel |
P29684
|
FINISHED |
| Object | low |
—
|
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: low | Statement: [Paris Métro Line 3bis, hasAverageRidershipLevel, low]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAverageRidershipLevel Context triple: [Paris Métro Line 3bis, hasAverageRidershipLevel, low]
-
A.
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.
-
B.
annualRidership
Indicates the total number of passengers who use a transportation service over the course of one year.
-
C.
ridershipLevel
chosen
Indicates the magnitude or intensity of usage by riders or passengers for a given service, route, or system.
-
D.
hasHeavyPassengerTraffic
Indicates that an entity experiences a high volume of passenger movement or usage over a given period.
-
E.
hasDailyPassengerTraffic
Indicates the number of passengers that regularly use or pass through something (such as a station or route) each day.
- 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_69f01a5f69d08190ad5c0d2167078dec |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f650542a1c8190b6f0e66be3bba62c |
completed | May 2, 2026, 7:28 p.m. |
| PD | Predicate disambiguation | batch_69f64cb0d8008190912e1430cfaf92aa |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 28, 2026, 4:05 a.m.