Triple
T38626216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MBTA tokens |
E937016
|
entity |
| Predicate | fareMediumFormFactor |
P9336
|
FINISHED |
| Object | coin-shaped |
—
|
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: coin-shaped | Statement: [MBTA tokens, fareMediumFormFactor, coin-shaped]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fareMediumFormFactor Context triple: [MBTA tokens, fareMediumFormFactor, coin-shaped]
-
A.
fareType
Indicates the category or class of fare (such as standard, discounted, or promotional) that applies to a given trip, ticket, or pricing instance.
-
B.
hasFormFactor
chosen
Indicates that one entity possesses or is characterized by a particular physical or structural form factor defined by another entity.
-
C.
fairType
Indicates the classification or category of a fair (e.g., type of event or exhibition) associated with an entity.
-
D.
fareBrand
Indicates the specific fare category or brand under which a ticket or booking is sold, defining its associated rules, benefits, and restrictions.
-
E.
fareModel
Indicates a pricing relationship where a specific fare structure, rule set, or calculation method is applied to determine the cost of a trip or service.
- 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_69fcdfbc71c481908ba7f87907b17782 |
completed | May 7, 2026, 6:53 p.m. |
| PD | Predicate disambiguation | batch_69fcdbe580b8819087f143596b2c79c0 |
completed | May 7, 2026, 6:37 p.m. |
Created at: May 3, 2026, 4:32 p.m.