Triple
T1909489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | International District/Chinatown Station |
E38075
|
entity |
| Predicate | hasTransitMode |
P31014
|
FINISHED |
| Object | light rail |
—
|
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: light rail | Statement: [International District/Chinatown Station, hasTransitMode, light rail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTransitMode Context triple: [International District/Chinatown Station, hasTransitMode, light rail]
-
A.
hasPublicTransitMode
chosen
Indicates that a location, route, or service is associated with or supports a specific mode of public transportation (e.g., bus, train, tram).
-
B.
hasTransportRoute
Indicates that there exists a designated transportation connection or route linking one entity to another.
-
C.
hasTransitFocus
Indicates that something is oriented toward, prioritizes, or is primarily concerned with transit or transportation services.
-
D.
hasFormOfPublicTransit
Indicates that one entity provides or is associated with a particular type or mode of public transportation for another entity or context.
-
E.
hasPublicTransitCoverageType
Indicates the type or category of public transit service coverage associated with an entity.
- 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_69a8862a26088190aae5243695aeefc0 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb34d94fc8190a5bf1e582c77c725 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafeba3d88190afcce67483d8625b |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:35 p.m.