Triple
T6154418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Keio fare system |
E137282
|
entity |
| Predicate | timeUnitForPasses |
P15625
|
FINISHED |
| Object | monthly |
—
|
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: monthly | Statement: [Keio fare system, timeUnitForPasses, monthly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeUnitForPasses Context triple: [Keio fare system, timeUnitForPasses, monthly]
-
A.
timeScaleUnit
Indicates the unit of temporal measurement (such as seconds, minutes, or hours) used to express a given time scale.
-
B.
timeUnitOfFrequency
chosen
Indicates the unit of time (e.g., day, week, month) in which a given frequency is measured or expressed.
-
C.
timeScaleType
Indicates the type or category of temporal scaling applied to an event, process, or measurement (e.g., real-time, accelerated, aggregated).
-
D.
timeScaleClass
Indicates the classification of a temporal scale or granularity at which an event, process, or relationship is considered or analyzed.
-
E.
timeSampling
Indicates that one entity specifies how or at what intervals another entity is sampled or measured over time.
- 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_69c008a45d008190832a9e19f5d63406 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05d01ddb0819085b5f5338b86a25d |
completed | March 22, 2026, 9:20 p.m. |
| PD | Predicate disambiguation | batch_69c055f39e0881909ae56444b1b48929 |
completed | March 22, 2026, 8:49 p.m. |
Created at: March 22, 2026, 4:17 p.m.