Triple
T14732851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RFK Bridge |
E346119
|
entity |
| Predicate | tollSystem |
P3913
|
FINISHED |
| Object | E‑ZPass |
E54620
|
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: E‑ZPass | Statement: [RFK Bridge, tollSystem, E‑ZPass]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: E‑ZPass Context triple: [RFK Bridge, tollSystem, E‑ZPass]
-
A.
E-ZPass
chosen
E-ZPass is an electronic toll collection system widely used on highways and bridges across the eastern United States, allowing drivers to pay tolls automatically without stopping.
-
B.
FasTrak
FasTrak is an electronic toll collection system used on bridges, roads, and express lanes throughout California.
-
C.
SunPass
SunPass is Florida’s statewide electronic toll collection system used on many of the state’s toll roads, bridges, and express lanes.
-
D.
TollTag
TollTag is an electronic toll collection system used on North Texas toll roads, allowing drivers to pay tolls automatically without stopping.
-
E.
ETC
ETC is a technology company specializing in simulation, training, and environmental control systems for aerospace, military, and industrial applications.
- 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec72ea9348190817efcdaa973d7f7 |
completed | April 14, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdfb8bcc188190901e3f692fd8fbf9 |
completed | May 8, 2026, 3:04 p.m. |
Created at: April 10, 2026, 1:29 a.m.