Triple
T4351756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Latifa |
E98041
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Latifa |
E98041
|
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: Latifa | Statement: [Latifa, name, Latifa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Latifa Context triple: [Latifa, name, Latifa]
-
A.
Latifa
chosen
Latifa is a young, imaginative girl character who serves as one of the official mascots of Expo 2020 Dubai, symbolizing innovation, curiosity, and the spirit of the event.
-
B.
Madali Khan
Madali Khan was a 19th-century ruler of the Kokand Khanate in Central Asia, known for his efforts to strengthen the state amid regional rivalries and Russian expansion.
-
C.
Sufiya Zinobia
Sufiya Zinobia is a central character in Salman Rushdie’s novel "Shame," symbolizing purity, repression, and the violent consequences of societal and familial pressures in a fictionalized Pakistan.
-
D.
Shabana
Shabana is a prominent Bangladeshi film actress renowned for her extensive and influential career in Bengali cinema.
-
E.
Naheed Mirza
Naheed Mirza was the wife of Iskander Mirza, the first President of Pakistan.
- 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_69b3454965f881908c41190bb22f0e4b |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b351a99788819080b13a20124e49a0 |
completed | March 12, 2026, 11:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5dbb01fb0819083fbdd194b2a96f8 |
completed | March 14, 2026, 10:05 p.m. |
Created at: March 12, 2026, 11:15 p.m.