Triple
T12542279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Earls Court station |
E299869
|
entity |
| Predicate | isInTravelcardZoneCombination |
P80296
|
FINISHED |
| Object | Zone 1/2 boundary |
—
|
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: Zone 1/2 boundary | Statement: [Earls Court station, isInTravelcardZoneCombination, Zone 1/2 boundary]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isInTravelcardZoneCombination Context triple: [Earls Court station, isInTravelcardZoneCombination, Zone 1/2 boundary]
-
A.
travelcardZoneCombination
chosen
Indicates the specific combination of fare zones in which a travelcard is valid or applicable.
-
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.
hasFareZone
Indicates that an entity is located within or associated with a specific fare zone used for pricing or ticketing.
-
D.
fareZoneIncludes
Indicates that a specified fare zone geographically or logically contains a given location, stop, or segment for fare calculation purposes.
-
E.
hasFareZoneFeature
Indicates that an entity is associated with a specific fare zone or fare-related area designation.
- 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_69d6ada707008190aaec1238117c9379 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95f5507b481908d13cc317b7402f6 |
completed | April 10, 2026, 8:36 p.m. |
| PD | Predicate disambiguation | batch_69d95410d0b0819097646edd1b837104 |
completed | April 10, 2026, 7:48 p.m. |
Created at: April 8, 2026, 9:57 p.m.