Triple
T21649602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barberton |
E534300
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | The Magic City |
—
|
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: The Magic City | Statement: [Barberton, nickname, The Magic City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Magic City Context triple: [Barberton, nickname, The Magic City]
-
A.
The Magic City
The Magic City is a nickname for Birmingham, Alabama, highlighting its rapid growth during the late 19th and early 20th centuries as an industrial and economic center.
-
B.
The Magic City
The Magic City is a nickname for Moberly, Missouri, a small American city historically known for its rapid growth as a railroad and industrial hub.
-
C.
The Magic City
chosen
The Magic City is a nickname for Barberton, Ohio, reflecting its rapid growth and development during the late 19th and early 20th centuries.
-
D.
Magic City
Magic City is the nickname of Billings, Montana, reflecting its rapid growth from a small railroad town into the state’s largest city.
-
E.
Magic City
Magic City is a popular nickname for Miami, highlighting the city's rapid growth, vibrant nightlife, and dynamic cultural scene.
- 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_69e0c466aec88190ba39c7543dbc8ba2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef5913cd9c81908a6ce9bc741416bf |
completed | April 27, 2026, 12:39 p.m. |
Created at: April 16, 2026, 6:35 p.m.