Triple
T3593535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CTA Green Line |
E76081
|
entity |
| Predicate | fareSystem |
P395
|
FINISHED |
| Object | Ventra |
E1909
|
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: Ventra | Statement: [CTA Green Line, fareSystem, Ventra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ventra Context triple: [CTA Green Line, fareSystem, Ventra]
-
A.
Ventra
chosen
Ventra is the contactless fare payment system used across Chicago’s public transit network, including buses and trains.
-
B.
Hertz
Hertz is a German surname most famously associated with physicist Heinrich Hertz, after whom the unit of frequency is named.
-
C.
Hertz
Hertz is one of the concert halls within the TivoliVredenburg music complex in Utrecht, known for hosting a variety of live performances and cultural events.
-
D.
Terminal 4S
Terminal 4S is the satellite terminal of Madrid’s Adolfo Suárez Madrid–Barajas Airport, primarily serving international and long-haul flights with modern, high-capacity facilities.
-
E.
Carris
Carris is the main public transport company in Lisbon, Portugal, operating the city's buses, trams, and certain historic lifts.
- 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_69ad85d8042081908af94a04c410dec0 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc15bbbcc81908d6cf95f8e70c6ca |
completed | March 8, 2026, 6:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4882803e481908bc716c8beda3c73 |
completed | March 13, 2026, 9:56 p.m. |
Created at: March 8, 2026, 3:22 p.m.