Triple
T26633476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zone D |
E668567
|
entity |
| Predicate | fareSystemRole |
P112377
|
FINISHED |
| Object | price differentiation by distance |
—
|
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: price differentiation by distance | Statement: [Zone D, fareSystemRole, price differentiation by distance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fareSystemRole Context triple: [Zone D, fareSystemRole, price differentiation by distance]
-
A.
fareSystem
Indicates a relationship where a system is used to determine, collect, or manage fares or payments for transportation or similar services.
-
B.
fareSystemFeature
chosen
Indicates that a fare system possesses or supports a particular feature, function, or characteristic related to how fares are calculated, managed, or used.
-
C.
fareSystemUse
Indicates the use or application of a particular fare system for travel, ticketing, or pricing.
-
D.
fareSystemPreviously
Indicates that a particular fare system existed or was in use at an earlier time relative to another fare system or time period.
-
E.
farePolicyRole
Indicates the role or function an entity has within a fare policy, such as how it participates in defining, applying, or managing that policy.
- 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_69ee9d0024b8819090a7c8cf669a3b6c |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f615ef22948190a908a048dc739479 |
completed | May 2, 2026, 3:19 p.m. |
| PD | Predicate disambiguation | batch_69f60b8bb0d08190ab5a9a2a8847c6f4 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 27, 2026, 2:26 a.m.