Triple
T19947953
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milnrow tram stop |
E479476
|
entity |
| Predicate | hasPeakTimeFrequency |
P115690
|
FINISHED |
| Object | every 6 minutes (typical) |
—
|
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 6 minutes (typical) | Statement: [Milnrow tram stop, hasPeakTimeFrequency, every 6 minutes (typical)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPeakTimeFrequency Context triple: [Milnrow tram stop, hasPeakTimeFrequency, every 6 minutes (typical)]
-
A.
hasPeakHourFrequency
chosen
Indicates how often a service or event occurs during designated peak hours.
-
B.
hasPeakHourFunction
Indicates that something performs a specific role or behavior during peak hours of activity or usage.
-
C.
hasPeakHourService
Indicates that a service operates or is available during designated peak or high-demand hours.
-
D.
hasPeakCount
Indicates the number of distinct peaks associated with an entity.
-
E.
hasPeakLoad
Indicates that an entity is associated with a maximum level of load or demand it experiences or can handle during a specific 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_69d8e522a17c819095165d4d24939fd8 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a68a32c8190ac9601db98594465 |
completed | April 20, 2026, 4:55 p.m. |
| PD | Predicate disambiguation | batch_69e537f47c508190853c4e009c6b5566 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:54 p.m.