Triple
T17174825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | METRO Park & Ride services |
E416830
|
entity |
| Predicate | typicalOperationFocus |
P31647
|
FINISHED |
| Object | peak commute periods |
—
|
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: peak commute periods | Statement: [METRO Park & Ride services, typicalOperationFocus, peak commute periods]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalOperationFocus Context triple: [METRO Park & Ride services, typicalOperationFocus, peak commute periods]
-
A.
focusesOn
Indicates that one entity directs its attention, effort, or primary activity toward another entity or specific subject.
-
B.
subjectOfOperation
Indicates that an entity is the primary focus, target, or object upon which an operation or procedure is performed.
-
C.
typicalOperator
Indicates that an entity commonly or normally performs operations on, or acts upon, another entity in a standard or expected manner.
-
D.
operatesPrimarilyOn
Indicates that one entity is mainly or chiefly used to act upon, affect, or process another entity.
-
E.
focusType
chosen
Indicates the specific kind or category of focus or attention that is being applied to or associated with an entity or interaction.
- 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_69d886d5f34c8190b24564dfaa63f3fb |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3fc0c329081909f118bd4b7be8653 |
completed | April 18, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69e383141ae0819096acd71683637cbc |
completed | April 18, 2026, 1:11 p.m. |
Created at: April 10, 2026, 5:37 a.m.