Triple
T14759663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris–Montparnasse suburban lines |
E346822
|
entity |
| Predicate | hasPeakHourFrequency |
P115690
|
FINISHED |
| Object | high frequency |
—
|
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: high frequency | Statement: [Paris–Montparnasse suburban lines, hasPeakHourFrequency, high frequency]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPeakHourFrequency Context triple: [Paris–Montparnasse suburban lines, hasPeakHourFrequency, high frequency]
-
A.
hasPeakHourFunction
Indicates that something performs a specific role or behavior during peak hours of activity or usage.
-
B.
hasPeakHourService
Indicates that a service operates or is available during designated peak or high-demand hours.
-
C.
hasPeak
Indicates that something possesses or contains a highest point, summit, or maximum value.
-
D.
hasPeakCount
Indicates the number of distinct peaks associated with an entity.
-
E.
offPeakServiceFrequency_minutes
Indicates the number of minutes between successive services during off-peak periods.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7f0f5a48190af008352c26574d7 |
completed | April 14, 2026, 11:04 p.m. |
| PD | Predicate disambiguation | batch_69de8c02e5c08190943c27594026faf7 |
completed | April 14, 2026, 6:48 p.m. |
| PDg | Predicate description generation | batch_69de8f4b67cc8190b84b59fcec5cf579 |
completed | April 14, 2026, 7:02 p.m. |
Created at: April 10, 2026, 1:30 a.m.