Triple
T36332320
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamburg Airport station |
E894682
|
entity |
| Predicate | hasServiceFrequencyPeak |
P115690
|
FINISHED |
| Object | every 10 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 10 minutes on line S1 | Statement: [Hamburg Airport station, hasServiceFrequencyPeak, every 10 minutes on line S1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasServiceFrequencyPeak Context triple: [Hamburg Airport station, hasServiceFrequencyPeak, every 10 minutes on line S1]
-
A.
hasPeakHourFrequency
chosen
Indicates how often a service or event occurs during designated peak hours.
-
B.
hasPeakHourService
Indicates that a service operates or is available during designated peak or high-demand hours.
-
C.
hasPeakHourFunction
Indicates that something performs a specific role or behavior during peak hours of activity or usage.
-
D.
hasPeakUse
Indicates that something reaches its highest or most intensive level of use during a particular time or condition.
-
E.
hasPeakOccasion
Indicates that something reaches its highest or most significant level, intensity, or occurrence during a particular occasion or event.
- 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_6a004fd3caf48190b2ec063a7bf0756b |
completed | May 10, 2026, 9:28 a.m. |
| PD | Predicate disambiguation | batch_6a004f7672dc8190aca91d1ed855bf9a |
completed | May 10, 2026, 9:27 a.m. |
Created at: May 3, 2026, 4:09 p.m.