Triple
T25672457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metra zone E |
E643714
|
entity |
| Predicate | fareZoneSystemOrder |
P158972
|
FINISHED |
| Object | outer zone relative to downtown Chicago |
—
|
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: outer zone relative to downtown Chicago | Statement: [Metra zone E, fareZoneSystemOrder, outer zone relative to downtown Chicago]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fareZoneSystemOrder Context triple: [Metra zone E, fareZoneSystemOrder, outer zone relative to downtown Chicago]
-
A.
fareZoneIncludes
Indicates that a specified fare zone geographically or logically contains a given location, stop, or segment for fare calculation purposes.
-
B.
fareZoneRange
Indicates the range of fare zones within which a ticket, pass, or fare rule is valid or applicable.
-
C.
fareZoneStart
Indicates the fare zone in which a journey, ticket, or pricing calculation begins.
-
D.
fareZoneDescription
Indicates the textual description of the fare zone associated with a service, location, or segment.
-
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. 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_69e77e7f69808190ad27df1006f6037a |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f5fb3389ac819092997022ed2bc2f8 |
completed | May 2, 2026, 1:25 p.m. |
| PD | Predicate disambiguation | batch_69f4807f8680819098a524158d049c63 |
completed | May 1, 2026, 10:29 a.m. |
| PDg | Predicate description generation | batch_69f48b9058d081908ec9af261ee092e2 |
completed | May 1, 2026, 11:16 a.m. |
Created at: April 21, 2026, 7:30 p.m.