Triple
T1057812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Porter (MBTA station) |
E22835
|
entity |
| Predicate | hasPassengerInformation |
P17090
|
FINISHED |
| Object | real-time arrival displays |
—
|
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: real-time arrival displays | Statement: [Porter (MBTA station), hasPassengerInformation, real-time arrival displays]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerInformation Context triple: [Porter (MBTA station), hasPassengerInformation, real-time arrival displays]
-
A.
hasPassengerInformationSystem
chosen
Indicates that an entity is equipped with a system that provides information to passengers, such as schedules, announcements, or travel updates.
-
B.
hasPassengerRole
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
-
C.
hasPassengerHandling
Indicates that an entity is responsible for or involved in managing the processes and services related to handling passengers.
-
D.
passengers
Indicates that one entity is traveling in or being transported by another entity, typically as a non-operating occupant.
-
E.
hasPassengerUsageCategory
Indicates the classification of how a passenger-related resource or service is used (e.g., its usage type or category for passengers).
- 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_69a493dada0481909c43649f9843ea91 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4ba6e35ac8190802341c31bda0e3b |
completed | March 1, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69a4b7340a048190807363f19d17a58f |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.