Triple
T15362506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Damen |
E367322
|
entity |
| Predicate | hasFareCardVendor |
P118273
|
FINISHED |
| Object | Ventra vending machines |
—
|
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: Ventra vending machines | Statement: [Damen, hasFareCardVendor, Ventra vending machines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFareCardVendor Context triple: [Damen, hasFareCardVendor, Ventra vending machines]
-
A.
hasFareZone
Indicates that an entity is located within or associated with a specific fare zone used for pricing or ticketing.
-
B.
hasFarePaidArea
Indicates that an entity includes or is associated with a zone where access is restricted to users who have paid a fare.
-
C.
hasFareZoneCode
Indicates that an entity is associated with a specific fare zone identifier used for pricing or tariff purposes.
-
D.
hasFareZoneSystem
Indicates that an entity uses or is associated with a particular fare zone system for determining travel costs or ticketing.
-
E.
hasCardNumber
Indicates that an entity is associated with, or assigned, a specific card number.
- F. None of above. chosen
Provenance (4 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e479f188190bbbc3dcd73853e02 |
completed | April 16, 2026, 1:41 a.m. |
| PD | Predicate disambiguation | batch_69deca991e5081908b0df3d1ee7d5338 |
completed | April 14, 2026, 11:15 p.m. |
| PDg | Predicate description generation | batch_69decf2e413481909d9180a8d78d2c17 |
completed | April 14, 2026, 11:35 p.m. |
Created at: April 10, 2026, 3:18 a.m.