Triple
T785598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | G train |
E16593
|
entity |
| Predicate | fareMedium |
P1303
|
FINISHED |
| Object | MetroCard |
E3073
|
NE 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: MetroCard | Statement: [G train, fareMedium, MetroCard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MetroCard Context triple: [G train, fareMedium, MetroCard]
-
A.
MetroCard
chosen
MetroCard is a magnetic stripe payment card formerly used as the primary method for paying fares on New York City’s public transit system, including subways and buses.
-
B.
SmarTrip
SmarTrip is a rechargeable contactless smart card used to pay fares on the Washington, D.C. region’s public transit systems.
-
C.
MTA New York City Subway fare system
The MTA New York City Subway fare system is the payment and ticketing framework that governs how riders pay to use New York City’s subway network, including methods like MetroCard and OMNY.
-
D.
Oyster card
The Oyster card is a rechargeable smartcard used for convenient, cashless payment on public transport services across London.
-
E.
Clipper card
The Clipper card is a reloadable contactless smart card used to pay fares across multiple public transit systems in the San Francisco Bay Area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a4936ad1fc81908f190208059ccf78 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a76b0d6c8190a09b1a0bd4a6eeec |
completed | March 1, 2026, 8:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a67efd612481909580fbef3605dcbc |
completed | March 3, 2026, 6:26 a.m. |
Created at: March 1, 2026, 7:38 p.m.