Triple
T5175746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Salma bint Abdullah |
E116793
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Salma |
E343693
|
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: Salma | Statement: [Princess Salma bint Abdullah, givenName, Salma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salma Context triple: [Princess Salma bint Abdullah, givenName, Salma]
-
A.
Salma
chosen
Salma is a feminine given name of Arabic origin, commonly used in various cultures around the world.
-
B.
Shabana
Shabana is a prominent Bangladeshi film actress renowned for her extensive and influential career in Bengali cinema.
-
C.
Zohra
Zohra is a character in Naguib Mahfouz’s novel "Miramar," which centers on the lives and conflicts of residents in a pension in Alexandria, Egypt.
-
D.
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.
-
E.
Riza Aziz
Riza Aziz is a Malaysian film producer and co-founder of Red Granite Pictures, known for financing high-profile Hollywood films and being embroiled in the 1MDB corruption scandal.
- 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_69bd446140f08190becb93c61158f27f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd797349008190b87ad9d0d3eb667f |
completed | March 20, 2026, 4:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bed94e269481908118fd1af1fc6a44 |
completed | March 21, 2026, 5:45 p.m. |
Created at: March 20, 2026, 1:45 p.m.