Triple
T6962806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moth Smoke |
E161414
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Daru Shezad
Daru Shezad is the disillusioned, downward-spiraling protagonist of Mohsin Hamid’s novel "Moth Smoke," set against the backdrop of contemporary Lahore’s social and economic inequalities.
|
E630511
|
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: Daru Shezad | Statement: [Moth Smoke, mainCharacter, Daru Shezad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daru Shezad Context triple: [Moth Smoke, mainCharacter, Daru Shezad]
-
A.
Arif Masood
Arif Masood is a Pakistani architect best known for designing the iconic Pakistan Monument in Islamabad.
-
B.
Abdul Mateen
Abdul Mateen is a Bruneian prince and public figure known for his military career, international diplomacy, and prominent presence in regional and global events.
-
C.
Raza Jaffrey
Raza Jaffrey is a British actor and singer known for his roles in television series such as "Smash," "Homeland," and "Spooks" (MI-5).
-
D.
Fahran Khan
Fahran Khan is a writer known for contributing to the song "Dirrty."
-
E.
Aasif Mandvi
Aasif Mandvi is a British-American actor, comedian, and writer best known for his work as a correspondent on The Daily Show and for roles in film, television, and theater.
- 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: Daru Shezad Triple: [Moth Smoke, mainCharacter, Daru Shezad]
Generated description
Daru Shezad is the disillusioned, downward-spiraling protagonist of Mohsin Hamid’s novel "Moth Smoke," set against the backdrop of contemporary Lahore’s social and economic inequalities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daru Shezad Target entity description: Daru Shezad is the disillusioned, downward-spiraling protagonist of Mohsin Hamid’s novel "Moth Smoke," set against the backdrop of contemporary Lahore’s social and economic inequalities.
-
A.
Arif Masood
Arif Masood is a Pakistani architect best known for designing the iconic Pakistan Monument in Islamabad.
-
B.
Abdul Mateen
Abdul Mateen is a Bruneian prince and public figure known for his military career, international diplomacy, and prominent presence in regional and global events.
-
C.
Raza Jaffrey
Raza Jaffrey is a British actor and singer known for his roles in television series such as "Smash," "Homeland," and "Spooks" (MI-5).
-
D.
Fahran Khan
Fahran Khan is a writer known for contributing to the song "Dirrty."
-
E.
Aasif Mandvi
Aasif Mandvi is a British-American actor, comedian, and writer best known for his work as a correspondent on The Daily Show and for roles in film, television, and theater.
- 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_69c68853cff881908439d488924a8283 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6daf197b0819085bd0433c8a7f716 |
completed | March 27, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7589c587c8190b97523b5ac2ab958 |
completed | March 28, 2026, 4:27 a.m. |
| NEDg | Description generation | batch_69c759553fe081909881c8d2ae680dfe |
completed | March 28, 2026, 4:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c759c79de48190bd3e079c07a9158a |
completed | March 28, 2026, 4:32 a.m. |
Created at: March 27, 2026, 2:30 p.m.