Triple
T7730244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shabana |
E175229
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Shabana |
E175229
|
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: Shabana | Statement: [Shabana, name, Shabana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shabana Context triple: [Shabana, name, Shabana]
-
A.
Shabana
chosen
Shabana is a prominent Bangladeshi film actress renowned for her extensive and influential career in Bengali cinema.
-
B.
Tirana Hassan
Tirana Hassan is a human rights lawyer and advocate who serves as the executive director of Human Rights Watch, leading global efforts to investigate and expose human rights abuses.
-
C.
Shahnaz Lalarukh
Shahnaz Lalarukh is the elder sister of Bollywood actor Shah Rukh Khan, known for maintaining a very private life away from the film industry.
-
D.
Salma
Salma is a feminine given name of Arabic origin, commonly used in various cultures around the world.
-
E.
Naseem
Naseem is the given name of Naseem Hamed, the British former professional boxer famed for his flamboyant style and knockout power.
- 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_69c6995e912c81909a49a2657103f786 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c703358cf881909df8496d943d6de7 |
completed | March 27, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b52e176481908595fea4ace7a607 |
completed | March 29, 2026, 5:14 a.m. |
Created at: March 27, 2026, 4:06 p.m.