Triple
T2631753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 8 (planned) |
E59646
|
entity |
| Predicate | hasPlannedServiceFrequency |
P28499
|
FINISHED |
| Object | high-frequency urban transit |
—
|
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 urban transit | Statement: [Line 8 (planned), hasPlannedServiceFrequency, high-frequency urban transit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPlannedServiceFrequency Context triple: [Line 8 (planned), hasPlannedServiceFrequency, high-frequency urban transit]
-
A.
hasDailyService
Indicates that a service or operation occurs every day on a regular, scheduled basis.
-
B.
hasEventFrequency
Indicates how often a particular event occurs within a given time period.
-
C.
performedFrequency
Indicates how often an action or activity is carried out within a given time period.
-
D.
hasCommunicationFrequency
Indicates how often communication occurs between the related entities.
-
E.
hasFrequencyCategory
chosen
Indicates that something is associated with a particular classification of how often it occurs or is used.
- 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_69ab4ac8596c8190b34997e73d9e991c |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abdb0e7b888190bfa5d2e33f00ec0f |
completed | March 7, 2026, 8 a.m. |
| PD | Predicate disambiguation | batch_69abd810d7f481908e81c305772c4c14 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:50 p.m.