Triple
T11107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washington Metro |
E225
|
entity |
| Predicate | fareCollection |
P395
|
FINISHED |
| Object | automatic faregates |
—
|
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: automatic faregates | Statement: [Washington Metro, fareCollection, automatic faregates]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fareCollection Context triple: [Washington Metro, fareCollection, automatic faregates]
-
A.
fareSystem
chosen
Indicates a relationship where a system is used to determine, collect, or manage fares or payments for transportation or similar services.
-
B.
funds
Indicates that one entity provides financial resources or monetary support to another entity or activity.
-
C.
venue
Indicates the place or location where an event, activity, or interaction takes place.
-
D.
drivesOn
Indicates that an entity uses or travels along a particular route, surface, or roadway as its path of movement.
-
E.
commemoratedBy
Indicates that something is honored, remembered, or celebrated through a particular action, event, object, or representation.
- 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_69a23d7ad88c8190bffe8ab091d86642 |
completed | Feb. 28, 2026, 12:57 a.m. |
| NER | Named-entity recognition | batch_69a242cd8fb481909562f114f4ce7700 |
completed | Feb. 28, 2026, 1:20 a.m. |
| PD | Predicate disambiguation | batch_69a23fe6b0bc8190bcce9b74f2c5fb08 |
completed | Feb. 28, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 1:02 a.m.