Triple
T38626220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MBTA tokens |
E937016
|
entity |
| Predicate | fareSystemTransitionTo |
P54837
|
FINISHED |
| Object | electronic fare collection |
—
|
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: electronic fare collection | Statement: [MBTA tokens, fareSystemTransitionTo, electronic fare collection]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fareSystemTransitionTo Context triple: [MBTA tokens, fareSystemTransitionTo, electronic fare collection]
-
A.
fareSystemChange
chosen
Indicates a change in the rules, structure, or method by which fares are calculated, collected, or applied.
-
B.
fareSystemPreviously
Indicates that a particular fare system existed or was in use at an earlier time relative to another fare system or time period.
-
C.
fareSystem
Indicates a relationship where a system is used to determine, collect, or manage fares or payments for transportation or similar services.
-
D.
fareSystemUse
Indicates the use or application of a particular fare system for travel, ticketing, or pricing.
-
E.
fareSystemFeature
Indicates that a fare system possesses or supports a particular feature, function, or characteristic related to how fares are calculated, managed, or used.
- 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_69f76ed403208190b862dc795171353f |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcdb0de8c08190928cd1323f80ab5c |
completed | May 7, 2026, 6:33 p.m. |
| PD | Predicate disambiguation | batch_69fcd9017dd88190b32a73fe78909740 |
completed | May 7, 2026, 6:25 p.m. |
Created at: May 3, 2026, 4:32 p.m.