Triple
T16527531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shabana Azmi |
E401479
|
entity |
| Predicate | givenName |
P17
|
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 Azmi, givenName, Shabana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shabana Context triple: [Shabana Azmi, givenName, 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.
Shibani Bathija
Shibani Bathija is an Indian screenwriter best known for writing popular Bollywood films such as "Fanaa" and "Kabhi Alvida Naa Kehna."
-
D.
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.
-
E.
Zabiba
Zabiba was the enslaved Ethiopian woman who became the mother of the famed pre-Islamic Arab poet and warrior Antarah ibn Shaddad.
- 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_69d883838abc8190bc79cb2d41733ce2 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32ed4b8a08190b5f179fc583001a6 |
completed | April 18, 2026, 7:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00608efd0c81908e64419bd74eb285 |
completed | May 10, 2026, 10:40 a.m. |
Created at: April 10, 2026, 5:14 a.m.