Triple
T7371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waverley station |
E146
|
entity |
| Predicate | hasFareZone |
P844
|
FINISHED |
| Object | Zone 1 |
—
|
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 | Statement: [Waverley station, hasFareZone, Zone 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFareZone Context triple: [Waverley station, hasFareZone, Zone 1]
-
A.
fareSystem
Indicates a relationship where a system is used to determine, collect, or manage fares or payments for transportation or similar services.
-
B.
hasRegion
Indicates that an entity includes, contains, or is associated with a specific geographic or administrative region as part of its scope or structure.
-
C.
hasAreaCode
Indicates that a specified telephone area code is assigned to or associated with a particular geographic region, location, or phone service entity.
-
D.
locatedInTimeZone
Indicates that an entity exists or an event occurs within the temporal bounds defined by a specific time zone.
-
E.
hasBusinessDistrict
Indicates that a place or administrative area contains or includes a designated business district within its boundaries.
- 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_69a23bb612708190b09f25385e4b63d1 |
completed | Feb. 28, 2026, 12:49 a.m. |
| NER | Named-entity recognition | batch_69a241a55ac081909e95b71c97db8140 |
completed | Feb. 28, 2026, 1:15 a.m. |
| PD | Predicate disambiguation | batch_69a23fe1cf38819080ea56c40bf2632e |
completed | Feb. 28, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69a241a4a0f481908de66b64c6262fcd |
completed | Feb. 28, 2026, 1:15 a.m. |
Created at: Feb. 28, 2026, 12:54 a.m.