Triple
T15645360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EasyCard |
E376162
|
entity |
| Predicate | supportsTransitDiscounts |
P119588
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [EasyCard, supportsTransitDiscounts, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsTransitDiscounts Context triple: [EasyCard, supportsTransitDiscounts, true]
-
A.
fareAppliesTo
Indicates that a specific fare is applicable to a particular trip, service, passenger category, or travel condition.
-
B.
fareDiscount
Indicates that a reduced price is applied to a standard fare for a product or service.
-
C.
supportsAgeDiscounts
Indicates that the subject entity offers or honors special pricing or discounts based on a person's age.
-
D.
appliesToTransitSystem
Indicates that something (such as a rule, policy, feature, or condition) is relevant or applicable to a particular transit or transportation system.
-
E.
appliesToTransitStation
Indicates that something (such as a rule, feature, condition, or attribute) is specifically relevant or applicable to a transit station.
- 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_69d85cd1564c8190991adda63bfab4b0 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ed400ec8190a14a9f7cf3092865 |
completed | April 16, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69deda890140819082608931e993dd61 |
completed | April 15, 2026, 12:23 a.m. |
| PDg | Predicate description generation | batch_69dff7f3016c8190ac68d76e65e07af4 |
completed | April 15, 2026, 8:41 p.m. |
Created at: April 10, 2026, 4:15 a.m.