Triple
T9951959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SkyTrain Millennium Line |
E195350
|
entity |
| Predicate | extension |
P11869
|
FINISHED |
| Object | Evergreen Extension |
E830192
|
NE 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: Evergreen Extension | Statement: [SkyTrain Millennium Line, extension, Evergreen Extension]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Evergreen Extension Context triple: [SkyTrain Millennium Line, extension, Evergreen Extension]
-
A.
Evergreen Extension
chosen
Evergreen Extension is a rapid transit extension of Metro Vancouver’s SkyTrain system that expanded the Millennium Line service into the Tri-Cities area.
-
B.
Evergreen
Evergreen is a literary imprint known for publishing innovative and influential works, particularly in avant-garde and progressive literature.
-
C.
Evergreen
Evergreen is a small city in southern Alabama that serves as the administrative and commercial center of Conecuh County.
-
D.
Evergreen
Evergreen is a 1934 British musical film directed by Victor Saville, known for its blend of romance, comedy, and popular songs of the era.
-
E.
EvergreenHealth
EvergreenHealth is a regional healthcare system based in Kirkland, Washington, known for operating hospitals and medical clinics that serve communities across the greater Seattle area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca82e96a108190932bd1fc4acd73a0 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb6922f888190b5c4b58fbe21bea2 |
completed | April 2, 2026, 12:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23d6743c481908a2040eb9d260b4a |
completed | April 5, 2026, 10:45 a.m. |
Created at: March 30, 2026, 8:46 p.m.