Triple
T2163392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 2 Bloor–Danforth |
E46850
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Warden station
Warden station is a subway station in Toronto, Ontario, serving as a key stop in the city's Bloor–Danforth line transit network.
|
E241672
|
NE FINISHED |
How this triple was built (4 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: Warden station | Statement: [Line 2 Bloor–Danforth, hasStation, Warden station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Warden station Context triple: [Line 2 Bloor–Danforth, hasStation, Warden station]
-
A.
Darien station
Darien station is a commuter rail stop on the Metro-North Railroad's New Haven Line serving the town of Darien, Connecticut.
-
B.
Hewlett station
Hewlett station is a Long Island Rail Road commuter rail stop serving the Hewlett community in Nassau County, New York.
-
C.
Edgewood station
Edgewood station is a commuter rail stop in Edgewood, Maryland, served by MARC’s Penn Line between Baltimore and Perryville.
-
D.
Logan station
Logan station is an underground rapid transit stop in Philadelphia, Pennsylvania, serving the Logan neighborhood on SEPTA’s Broad Street Line.
-
E.
Snyder station
Snyder station is an underground rapid transit stop on SEPTA’s Broad Street Line serving South Philadelphia.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Warden station Triple: [Line 2 Bloor–Danforth, hasStation, Warden station]
Generated description
Warden station is a subway station in Toronto, Ontario, serving as a key stop in the city's Bloor–Danforth line transit network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Warden station Target entity description: Warden station is a subway station in Toronto, Ontario, serving as a key stop in the city's Bloor–Danforth line transit network.
-
A.
Darien station
Darien station is a commuter rail stop on the Metro-North Railroad's New Haven Line serving the town of Darien, Connecticut.
-
B.
Hewlett station
Hewlett station is a Long Island Rail Road commuter rail stop serving the Hewlett community in Nassau County, New York.
-
C.
Edgewood station
Edgewood station is a commuter rail stop in Edgewood, Maryland, served by MARC’s Penn Line between Baltimore and Perryville.
-
D.
Logan station
Logan station is an underground rapid transit stop in Philadelphia, Pennsylvania, serving the Logan neighborhood on SEPTA’s Broad Street Line.
-
E.
Snyder station
Snyder station is an underground rapid transit stop on SEPTA’s Broad Street Line serving South Philadelphia.
- F. None of above. chosen
Provenance (5 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_69a88a184cbc8190877791f6552c2484 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbe8d105c819098371c35c88873dc |
completed | March 7, 2026, 5:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5d98286c8190a6890e2c7daa10b5 |
completed | March 9, 2026, 5:41 a.m. |
| NEDg | Description generation | batch_69ae5e30a69c8190a3f77e784401f671 |
completed | March 9, 2026, 5:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae5ea4edcc81908829e4bd64ce0aea |
completed | March 9, 2026, 5:46 a.m. |
Created at: March 4, 2026, 7:45 p.m.