Triple
T36332321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamburg Airport station |
E894682
|
entity |
| Predicate | hasServiceFrequencyOffPeak |
P114667
|
FINISHED |
| Object | every 20 minutes on line S1 |
—
|
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: every 20 minutes on line S1 | Statement: [Hamburg Airport station, hasServiceFrequencyOffPeak, every 20 minutes on line S1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasServiceFrequencyOffPeak Context triple: [Hamburg Airport station, hasServiceFrequencyOffPeak, every 20 minutes on line S1]
-
A.
offPeakServiceFrequency_minutes
chosen
Indicates the number of minutes between successive services during off-peak periods.
-
B.
hasOffPeakUsage
Indicates that an entity exhibits or is associated with usage occurring during designated off-peak times rather than standard peak periods.
-
C.
hasPeakOffPeakDifferentiation
Indicates that there is a distinction between peak and off-peak periods in how something is applied, priced, or operated.
-
D.
hasPeakHourFrequency
Indicates how often a service or event occurs during designated peak hours.
-
E.
hasPeakHourService
Indicates that a service operates or is available during designated peak or high-demand hours.
- 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_69f76e4dcf088190a6c3216c209cab52 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a00512437d48190ad20324968ead5f4 |
completed | May 10, 2026, 9:34 a.m. |
| PD | Predicate disambiguation | batch_6a0050227350819099f41369c3d168be |
completed | May 10, 2026, 9:30 a.m. |
Created at: May 3, 2026, 4:09 p.m.