Triple
T9185116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Powell & Market cable car turnaround |
E220436
|
entity |
| Predicate | hasQueueManagement |
P24240
|
FINISHED |
| Object | roped-off waiting areas |
—
|
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: roped-off waiting areas | Statement: [Powell & Market cable car turnaround, hasQueueManagement, roped-off waiting areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasQueueManagement Context triple: [Powell & Market cable car turnaround, hasQueueManagement, roped-off waiting areas]
-
A.
hasQueue
Indicates that an entity maintains or is associated with a queue, typically representing an ordered list of items or tasks awaiting processing.
-
B.
usesQueue
Indicates that one entity employs or relies on a queue mechanism to manage or process items, tasks, or messages.
-
C.
hasStationManagement
Indicates that one entity is responsible for managing, operating, or overseeing the activities and administration of a station associated with another entity.
-
D.
hasQueueType
chosen
Indicates that an entity is associated with a specific type or category of queue.
-
E.
hasTrafficManagement
Indicates that an entity implements, uses, or is associated with systems or measures for controlling and optimizing traffic flow.
- 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_69ca83e6d77c81909862b7afef56b1bf |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccc318d5c081908c50b56e5783ea38 |
completed | April 1, 2026, 7:02 a.m. |
| PD | Predicate disambiguation | batch_69cc66090e5881908889dc1213815626 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:24 p.m.