Triple
T15510845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kedzie |
E368703
|
entity |
| Predicate | hasFarecardVending |
P118273
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Kedzie, hasFarecardVending, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFarecardVending Context triple: [Kedzie, hasFarecardVending, yes]
-
A.
hasFareCardVendor
chosen
Indicates that an entity provides or is associated with a machine or service point where fare cards can be purchased, reloaded, or otherwise obtained.
-
B.
hasAutomaticFareCollection
Indicates that an entity is equipped with a system that automatically collects fares or payments from users without manual processing.
-
C.
hasVIPTerminal
Indicates that one entity possesses or provides access to a VIP (very important person) terminal associated with another entity.
-
D.
hasFaregates
Indicates that an entity is equipped with or contains faregates used to control or validate access, typically for paid entry.
-
E.
hasFareZoneSystem
Indicates that an entity uses or is associated with a particular fare zone system for determining travel costs or ticketing.
- F. None of above.
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_69d85a1794cc8190b0b428716296e63e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03fd008708190a3657863eb9ac626 |
completed | April 16, 2026, 1:48 a.m. |
| PD | Predicate disambiguation | batch_69ded2896a9c8190a8b9627deb3c17b4 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:55 a.m.