Triple
T22021917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Devil’s Harvest |
E543865
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Tamer Hassan |
—
|
NE NERFINISHED |
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: Tamer Hassan | Statement: [The Devil’s Harvest, castMember, Tamer Hassan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamer Hassan Context triple: [The Devil’s Harvest, castMember, Tamer Hassan]
-
A.
Tamer Hassan
chosen
Tamer Hassan is a British actor known for his tough-guy roles in crime and gangster films.
-
B.
Kamal Elgargni
Kamal Elgargni is a Libyan professional bodybuilder best known for winning the 212 division title at the Mr. Olympia competition.
-
C.
Tamer Hosny
Tamer Hosny is an Egyptian singer, actor, and composer widely regarded as one of the most popular contemporary Arabic pop stars.
-
D.
Tarek Sharif
Tarek Sharif is the son of legendary Egyptian actors Omar Sharif and Faten Hamama.
-
E.
Alexander Siddig
Alexander Siddig is a Sudanese-born British actor known for his roles in film and television, including prominent performances in "Star Trek: Deep Space Nine," "Syriana," and "Game of Thrones."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e2e8ea4819084210fe06d3a1b8d |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127c8ac6881909a9e96e0873a3ae2 |
completed | April 28, 2026, 9:34 p.m. |
Created at: April 16, 2026, 8:23 p.m.