Triple
T5553232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 湘南新宿ライン |
E145575
|
entity |
| Predicate | 運賃体系 |
P32811
|
FINISHED |
| Object | JR在来線普通運賃 |
—
|
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: JR在来線普通運賃 | Statement: [湘南新宿ライン, 運賃体系, JR在来線普通運賃]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 運賃体系 Context triple: [湘南新宿ライン, 運賃体系, JR在来線普通運賃]
-
A.
fareStructure
chosen
Indicates the pricing scheme or set of rules that determine how fares are calculated and applied for a given service or trip.
-
B.
fareBasis
Indicates the specific fare rule or pricing category that applies to a ticket or travel segment.
-
C.
fareStructureFeature
Indicates a characteristic or condition of how fares are structured, calculated, or applied within a pricing or ticketing system.
-
D.
fareType
Indicates the category or class of fare (such as standard, discounted, or promotional) that applies to a given trip, ticket, or pricing instance.
-
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.
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_69c008fb879c81909f5bfa56fadc1d46 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01ff9c9c48190b5e587d58c6515d8 |
completed | March 22, 2026, 4:59 p.m. |
| PD | Predicate disambiguation | batch_69c01b10bbf8819098655839c03b7832 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:35 p.m.