Triple
T17312635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MAX Light Rail |
E420336
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object | MAX Blue Line |
—
|
NE NERFINISHED |
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: MAX Blue Line | Statement: [MAX Light Rail, hasLine, MAX Blue Line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MAX Blue Line Context triple: [MAX Light Rail, hasLine, MAX Blue Line]
-
A.
MAX Blue Line
chosen
MAX Blue Line is a light rail service in the Portland, Oregon metropolitan area that connects downtown Portland with eastern and western suburbs as part of the region’s MAX Light Rail system.
-
B.
Blue Line
The Blue Line is one of the main lines of the Lisbon Metro system, serving key central and northern areas of Portugal’s capital city.
-
C.
Blue Line
The Blue Line is one of the color-coded rapid transit routes in the Washington Metro system, running through key parts of Washington, D.C. and its Virginia suburbs.
-
D.
Blue Line
The Blue Line is a light rail route in the Dallas Area Rapid Transit (DART) system serving key neighborhoods and suburbs in the Dallas–Fort Worth metroplex.
-
E.
Blue Line
The Blue Line is one of the automated people-mover routes in San Francisco International Airport’s AirTrain system, circulating between terminals, parking garages, and other key airport facilities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889d22b848190a4663d0b8f8f76e7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4399a4194819091d34cd3fffc8072 |
completed | April 19, 2026, 2:10 a.m. |
Created at: April 10, 2026, 5:43 a.m.