Triple
T15748585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MTS |
E381786
|
entity |
| Predicate | fareSystem |
P395
|
FINISHED |
| Object | Pronto |
E381796
|
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: Pronto | Statement: [MTS, fareSystem, Pronto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pronto Context triple: [MTS, fareSystem, Pronto]
-
A.
Pronto
Pronto is a crime novel by Elmore Leonard that introduces the character U.S. Marshal Raylan Givens in a fast-paced story of mobsters, hitmen, and a bookie on the run.
-
B.
Pronto
"Pronto" is a hip hop single released by American rapper Snoop Dogg.
-
C.
PRONTO
chosen
PRONTO is a contactless, account-based fare payment system used for public transit services in San Diego County, California.
-
D.
Rapido
Rapido is an Indian app-based bike taxi and logistics platform that offers affordable two-wheeler rides and deliveries in urban areas.
-
E.
Hasten
Hasten is a district of the German city of Remscheid, known for its historic buildings and traditional Bergisches architecture.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0502fd3608190b42e647b9c2b41a1 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff830b85408190b9ae4d6752524b99 |
completed | May 9, 2026, 6:55 p.m. |
Created at: April 10, 2026, 4:46 a.m.