Triple
T3939794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlotte Hornets |
E92005
|
entity |
| Predicate | hasFanBaseNickname |
P5382
|
FINISHED |
| Object |
Buzz City
Buzz City is the popular nickname for the passionate fan base and home atmosphere surrounding the NBA’s Charlotte Hornets.
|
E399505
|
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: Buzz City | Statement: [Charlotte Hornets, hasFanBaseNickname, Buzz City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buzz City Context triple: [Charlotte Hornets, hasFanBaseNickname, Buzz City]
-
A.
Metro City
Metro City is the fictional, superhero-populated metropolis that serves as the primary setting of the animated film "Megamind."
-
B.
Bell City
Bell City is a nickname for Bristol, Connecticut, historically known for its prominent clock and bell manufacturing industry.
-
C.
Hat City
Hat City is the nickname of Danbury, Connecticut, reflecting its historic prominence as a major center of hat manufacturing in the United States.
-
D.
Gateway City
Gateway City is a nickname for St. Louis, Missouri, highlighting its historic role as a major entry point to the American West.
-
E.
Chocolate City
Chocolate City is a popular nickname for Washington, D.C., highlighting its historically large and influential African American population and culture.
- 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: Buzz City Triple: [Charlotte Hornets, hasFanBaseNickname, Buzz City]
Generated description
Buzz City is the popular nickname for the passionate fan base and home atmosphere surrounding the NBA’s Charlotte Hornets.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Buzz City Target entity description: Buzz City is the popular nickname for the passionate fan base and home atmosphere surrounding the NBA’s Charlotte Hornets.
-
A.
Metro City
Metro City is the fictional, superhero-populated metropolis that serves as the primary setting of the animated film "Megamind."
-
B.
Bell City
Bell City is a nickname for Bristol, Connecticut, historically known for its prominent clock and bell manufacturing industry.
-
C.
Hat City
Hat City is the nickname of Danbury, Connecticut, reflecting its historic prominence as a major center of hat manufacturing in the United States.
-
D.
Gateway City
Gateway City is a nickname for St. Louis, Missouri, highlighting its historic role as a major entry point to the American West.
-
E.
Chocolate City
Chocolate City is a popular nickname for Washington, D.C., highlighting its historically large and influential African American population and culture.
- 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_69aed965502c8190904ebad1203a4ae8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeedfb12b88190a6ca6574b1aadb6e |
completed | March 9, 2026, 3:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b52894cb94819099d023745fcaebbc |
completed | March 14, 2026, 9:21 a.m. |
| NEDg | Description generation | batch_69b5293c3a0c8190abe1924ecc12dc46 |
completed | March 14, 2026, 9:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b529f6a3488190a7a9ae37f71cff56 |
completed | March 14, 2026, 9:27 a.m. |
Created at: March 9, 2026, 3:24 p.m.