Triple
T14240255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | We Are Lady Parts |
E352984
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Ayesha
Ayesha is a central character in the British sitcom "We Are Lady Parts," known as the confident, rebellious lead guitarist of the all-female Muslim punk band.
|
E1088857
|
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: Ayesha | Statement: [We Are Lady Parts, mainCharacter, Ayesha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ayesha Context triple: [We Are Lady Parts, mainCharacter, Ayesha]
-
A.
Ayesha
Ayesha is a central fictional heroine in Bankim Chandra Chattopadhyay’s historical Bengali novel "Durgeshnandini," known for her beauty, courage, and tragic love.
-
B.
Laila
Laila is a feminine given name used in various cultures, often associated with meanings like "night" or "dark beauty."
-
C.
Shireen
Shireen is a feminine given name of Persian origin, commonly used in various cultures across the Middle East and South Asia.
-
D.
Aisha
Aisha is a skilled and enigmatic operative who joins the elite black-ops team in the action film "The Losers" (2010).
-
E.
Aisha
Aisha is a central female character in Naguib Mahfouz’s novel "Palace of Desire," whose relationships and personal struggles reflect the broader social and emotional tensions of early 20th-century Cairo.
- 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: Ayesha Triple: [We Are Lady Parts, mainCharacter, Ayesha]
Generated description
Ayesha is a central character in the British sitcom "We Are Lady Parts," known as the confident, rebellious lead guitarist of the all-female Muslim punk band.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ayesha Target entity description: Ayesha is a central character in the British sitcom "We Are Lady Parts," known as the confident, rebellious lead guitarist of the all-female Muslim punk band.
-
A.
Ayesha
Ayesha is a central fictional heroine in Bankim Chandra Chattopadhyay’s historical Bengali novel "Durgeshnandini," known for her beauty, courage, and tragic love.
-
B.
Laila
Laila is a feminine given name used in various cultures, often associated with meanings like "night" or "dark beauty."
-
C.
Shireen
Shireen is a feminine given name of Persian origin, commonly used in various cultures across the Middle East and South Asia.
-
D.
Aisha
Aisha is a central female protagonist in Naguib Mahfouz’s novel "Palace Walk," representing the complexities of family life and social change in early 20th-century Cairo.
-
E.
Aisha
Aisha is a singer who performed one of the official songs for the 2022 FIFA World Cup.
- 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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de62432fb48190b153805b85c4f2d2 |
completed | April 14, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3253fc2c8190ba2da6fe6a910d85 |
completed | May 8, 2026, 12:46 a.m. |
| NEDg | Description generation | batch_69fd33acbce88190ac84ad188aa24726 |
completed | May 8, 2026, 12:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd342b64b88190b85383f6f51970ed |
completed | May 8, 2026, 12:54 a.m. |
Created at: April 10, 2026, 1:08 a.m.