Triple
T33601229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BART distance-based fare system |
E860724
|
entity |
| Predicate | fareVariesBy |
P98150
|
FINISHED |
| Object | time of day (for some promotional or special fares) |
—
|
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: time of day (for some promotional or special fares) | Statement: [BART distance-based fare system, fareVariesBy, time of day (for some promotional or special fares)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fareVariesBy Context triple: [BART distance-based fare system, fareVariesBy, time of day (for some promotional or special fares)]
-
A.
rateVariesBy
chosen
Indicates that the rate of something changes depending on a specified factor, condition, or category.
-
B.
termVariesBy
Indicates that the value or meaning of a term changes depending on a specified factor, such as context, dimension, or condition.
-
C.
tollVariesBy
Indicates that the amount of a toll changes depending on a specified factor or condition (such as time, vehicle type, or route).
-
D.
formatVariesBy
Indicates that the format or structure of something changes depending on a specified condition, context, or parameter.
-
E.
usageVariesBy
Indicates that the way something is used differs depending on a specified factor, such as context, user, location, or conditions.
- 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_69f3497f35908190a2e9bbb9b96c7a3f |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f7ac100081909d34cd985a008155 |
completed | May 3, 2026, 7:22 a.m. |
| PD | Predicate disambiguation | batch_69f6f6632dfc8190af85e258c8519207 |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:41 a.m.