Triple
T9249200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Downtown Inglewood station |
E222275
|
entity |
| Predicate | isWithinFareZone |
P844
|
FINISHED |
| Object | Metro A/B system flat fare |
—
|
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: Metro A/B system flat fare | Statement: [Downtown Inglewood station, isWithinFareZone, Metro A/B system flat fare]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isWithinFareZone Context triple: [Downtown Inglewood station, isWithinFareZone, Metro A/B system flat fare]
-
A.
hasFareZone
chosen
Indicates that an entity is located within or associated with a specific fare zone used for pricing or ticketing.
-
B.
isWithinLondonFareSystem
Indicates that an entity (such as a station, stop, or route) is located inside the area covered by the London public transport fare system.
-
C.
fareZoneIncludes
Indicates that a specified fare zone geographically or logically contains a given location, stop, or segment for fare calculation purposes.
-
D.
hasFareZoneFeature
Indicates that an entity is associated with a specific fare zone or fare-related area designation.
-
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_69ca841d2b18819089f9faf5b2c2aec0 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd05f7e9848190939f9199d0c1a572 |
completed | April 1, 2026, 11:48 a.m. |
| PD | Predicate disambiguation | batch_69cc7a4e79e48190b3200247f4624867 |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:31 p.m.