Triple
T3543586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CTA Blue Line |
E74942
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Racine
Racine is a Chicago Transit Authority Blue Line rapid transit station serving the Near West Side of Chicago.
|
E377132
|
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: Racine | Statement: [CTA Blue Line, hasStation, Racine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Racine Context triple: [CTA Blue Line, hasStation, Racine]
-
A.
Racine
Racine is a city in southeastern Wisconsin located on the shore of Lake Michigan, known historically for its manufacturing industry and Danish kringle pastries.
-
B.
Kenosha
Kenosha is a mid-sized city in southeastern Wisconsin located on the shore of Lake Michigan between Milwaukee and Chicago.
-
C.
Milwaukie
Milwaukie is a small city in northwestern Oregon, located just south of Portland along the Willamette River.
-
D.
Milwaukee
Milwaukee is the largest city in Wisconsin, known for its brewing traditions, industrial history, and location on the western shore of Lake Michigan.
-
E.
Marinette, Wisconsin
Marinette, Wisconsin is a small industrial city in northeastern Wisconsin on the shore of Green Bay, known historically for shipbuilding and its location opposite Menominee, Michigan.
- 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: Racine Triple: [CTA Blue Line, hasStation, Racine]
Generated description
Racine is a Chicago Transit Authority Blue Line rapid transit station serving the Near West Side of Chicago.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Racine Target entity description: Racine is a Chicago Transit Authority Blue Line rapid transit station serving the Near West Side of Chicago.
-
A.
Racine
Racine is a city in southeastern Wisconsin located on the shore of Lake Michigan, known historically for its manufacturing industry and Danish kringle pastries.
-
B.
Kenosha
Kenosha is a mid-sized city in southeastern Wisconsin located on the shore of Lake Michigan between Milwaukee and Chicago.
-
C.
Milwaukie
Milwaukie is a small city in northwestern Oregon, located just south of Portland along the Willamette River.
-
D.
Milwaukee
Milwaukee is the largest city in Wisconsin, known for its brewing traditions, industrial history, and location on the western shore of Lake Michigan.
-
E.
Marinette, Wisconsin
Marinette, Wisconsin is a small industrial city in northeastern Wisconsin on the shore of Green Bay, known historically for shipbuilding and its location opposite Menominee, Michigan.
- 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_69ad85d274cc8190ab59c97298a1cfbf |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbf752dd481909226044ffe595338 |
completed | March 8, 2026, 6:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b48823ca248190a34d2d0eb3a496a7 |
completed | March 13, 2026, 9:56 p.m. |
| NEDg | Description generation | batch_69b48bb81ee88190a285c847c85768dd |
completed | March 13, 2026, 10:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4a42c67848190956660cd64e5b8e0 |
completed | March 13, 2026, 11:56 p.m. |
Created at: March 8, 2026, 3:20 p.m.