Triple
T14556249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rennes Metro |
E341548
|
entity |
| Predicate | peakServiceFrequency_minutes |
P55256
|
FINISHED |
| Object | about 1.5 to 3 |
—
|
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: about 1.5 to 3 | Statement: [Rennes Metro, peakServiceFrequency_minutes, about 1.5 to 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakServiceFrequency_minutes Context triple: [Rennes Metro, peakServiceFrequency_minutes, about 1.5 to 3]
-
A.
peakServicePeriod
Indicates the time interval during which a service experiences its highest or most intensive level of use or operation.
-
B.
serviceFrequencyType
chosen
Indicates how often a service occurs or is scheduled within a given time period.
-
C.
serviceFrequencyContext
Indicates the contextual conditions or circumstances under which a service’s frequency is defined, applied, or interpreted.
-
D.
rotationPeriod_hours
Indicates the length of time, measured in hours, that an object takes to complete one full rotation on its axis.
-
E.
peakService
Indicates that the service is operating during its highest-demand or peak usage period.
- 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_69d822db9c8481908213ceb39585f792 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb2f1490881908673f429e5288c86 |
completed | April 14, 2026, 9:34 p.m. |
| PD | Predicate disambiguation | batch_69de5c57489c8190b57917be1dba6ae6 |
completed | April 14, 2026, 3:25 p.m. |
Created at: April 10, 2026, 1:23 a.m.