Triple
T7333991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Omaha |
E169077
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object | Magic City |
E257560
|
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: Magic City | Statement: [South Omaha, hasNickname, Magic City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Magic City Context triple: [South Omaha, hasNickname, Magic City]
-
A.
Magic City
chosen
Magic City is the nickname of Billings, Montana, reflecting its rapid growth from a small railroad town into the state’s largest city.
-
B.
Magic City
Magic City is a popular nickname for Miami, highlighting the city's rapid growth, vibrant nightlife, and dynamic cultural scene.
-
C.
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.
-
D.
Winter City
Winter City is the popular nickname for Östersund, a Swedish town renowned for its cold climate and strong winter sports culture.
-
E.
Collar City
Collar City is the nickname for Troy, New York, historically known as a major center of shirt-collar and textile manufacturing.
- 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_69c68a568a6481908f11e20db7bc8446 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f0c25758819095aa5041c6ecff07 |
completed | March 27, 2026, 9:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7ef1eb370819081a4c6e75ceaf2eb |
completed | March 28, 2026, 3:09 p.m. |
Created at: March 27, 2026, 3:04 p.m.