Triple
T34530524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metra fare zone H |
E886523
|
entity |
| Predicate | fareDifferentiationFrom |
P179280
|
FINISHED |
| Object | Metra fare zone A |
—
|
NE NERFINISHED |
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: Metra fare zone A | Statement: [Metra fare zone H, fareDifferentiationFrom, Metra fare zone A]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fareDifferentiationFrom Context triple: [Metra fare zone H, fareDifferentiationFrom, Metra fare zone A]
-
A.
fareType
Indicates the category or class of fare (such as standard, discounted, or promotional) that applies to a given trip, ticket, or pricing instance.
-
B.
fareTypes
Indicates the categories or kinds of fares (e.g., ticket or pricing options) that apply to a given travel or service offering.
-
C.
fareBasis
Indicates the specific fare rule or pricing category that applies to a ticket or travel segment.
-
D.
fareContext
Indicates the pricing or fare-related conditions under which a transaction, service, or travel arrangement is applied.
-
E.
fareModel
Indicates a pricing relationship where a specific fare structure, rule set, or calculation method is applied to determine the cost of a trip or service.
- 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_69f349cd7c148190aa99192b126d1527 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7201e241c819092d56a7bb99dc94d |
completed | May 3, 2026, 10:14 a.m. |
| PD | Predicate disambiguation | batch_69f71cc8074c81909ae09bea2acf1a09 |
completed | May 3, 2026, 10 a.m. |
| PDg | Predicate description generation | batch_69f71f8df5d48190944fbfbd9d573868 |
completed | May 3, 2026, 10:12 a.m. |
Created at: May 1, 2026, 2:02 a.m.