Triple
T11235830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nadim Sawalha |
E265938
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Nadim Sawalha |
E265938
|
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: Nadim Sawalha | Statement: [Nadim Sawalha, name, Nadim Sawalha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nadim Sawalha Context triple: [Nadim Sawalha, name, Nadim Sawalha]
-
A.
Nadim Sawalha
chosen
Nadim Sawalha is a Jordanian-British actor known for his character roles in film and television, including appearances in James Bond movies and various British dramas.
-
B.
Tarek Sharif
Tarek Sharif is the son of legendary Egyptian actors Omar Sharif and Faten Hamama.
-
C.
Adil Hussain
Adil Hussain is an Indian actor known for his nuanced performances in both Indian and international films, as well as in theatre and television.
-
D.
Nadia Sawalha
Nadia Sawalha is a British actress and television presenter best known for her long-running role as a panellist on the daytime talk show "Loose Women."
-
E.
Nabil Elderkin
Nabil Elderkin is an acclaimed photographer and music video director known for his visually striking work with artists such as Kanye West, Frank Ocean, and John Legend.
- 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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e904cf888190826fc964f76b5cb2 |
completed | April 9, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4ad6308f8819085652d6c529ac821 |
completed | April 19, 2026, 10:24 a.m. |
Created at: April 8, 2026, 9:30 p.m.