Triple
T22189668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | "Bye, Felicia" |
E548383
|
entity |
| Predicate | associatedWithCharacter |
P1481
|
FINISHED |
| Object |
Felisha
Felisha is the character from the 1995 film "Friday" whose dismissive send-off inspired the popular catchphrase "Bye, Felicia."
|
E1524641
|
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: Felisha | Statement: ["Bye, Felicia", associatedWithCharacter, Felisha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Felisha Context triple: ["Bye, Felicia", associatedWithCharacter, Felisha]
-
A.
Felecia
Felecia is an American R&B and hip-hop singer best known for her collaborations with Bone Thugs-N-Harmony in the 1990s and early 2000s.
-
B.
Keisha
Keisha is a feminine given name used in English-speaking communities, often associated with African-American culture.
-
C.
Felicia
Felicia is a feminine given name of Latin origin meaning "happy" or "fortunate," used in various cultures around the world.
-
D.
Dameisha
Dameisha is a popular coastal area in Shenzhen, China, best known for its long sandy beach, seaside resorts, and recreational attractions.
-
E.
Shanna
Shanna is a character from Quentin Tarantino's film "Death Proof," known for being one of the women targeted by the murderous stunt driver Stuntman Mike.
- 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: Felisha Triple: ["Bye, Felicia", associatedWithCharacter, Felisha]
Generated description
Felisha is the character from the 1995 film "Friday" whose dismissive send-off inspired the popular catchphrase "Bye, Felicia."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Felisha Target entity description: Felisha is the character from the 1995 film "Friday" whose dismissive send-off inspired the popular catchphrase "Bye, Felicia."
-
A.
Felecia
Felecia is an American R&B and hip-hop singer best known for her collaborations with Bone Thugs-N-Harmony in the 1990s and early 2000s.
-
B.
Keisha
Keisha is a feminine given name used in English-speaking communities, often associated with African-American culture.
-
C.
Felicia
Felicia is a feminine given name of Latin origin meaning "happy" or "fortunate," used in various cultures around the world.
-
D.
Dameisha
Dameisha is a popular coastal area in Shenzhen, China, best known for its long sandy beach, seaside resorts, and recreational attractions.
-
E.
Shanna
Shanna is a character from Quentin Tarantino's film "Death Proof," known for being one of the women targeted by the murderous stunt driver Stuntman Mike.
- 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_69e11e3e0c7c8190b30d278845e2497e |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12aac07d88190848c940863c0a0c7 |
completed | April 28, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0aa60c29608190afef2bcae36e4c12 |
completed | May 18, 2026, 5:39 a.m. |
| NEDg | Description generation | batch_6a0aa73b63008190955cdfc17063c405 |
completed | May 18, 2026, 5:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0aa7ab0d108190a907cba7075bddd8 |
completed | May 18, 2026, 5:46 a.m. |
Created at: April 16, 2026, 8:35 p.m.