Triple
T22658144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | رشدي أباظة |
E559286
|
entity |
| Predicate | deathPlace |
P21
|
FINISHED |
| Object | القاهرة |
—
|
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: القاهرة | Statement: [رشدي أباظة, deathPlace, القاهرة]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: القاهرة Context triple: [رشدي أباظة, deathPlace, القاهرة]
-
A.
Cairo
chosen
Cairo is the capital and largest city of Egypt, a historic metropolis on the Nile renowned for its rich Islamic heritage and proximity to the ancient pyramids.
-
B.
Cairo
"Cairo" is a 1942 American musical comedy film starring Jeanette MacDonald as a reporter entangled in wartime espionage and romantic intrigue.
-
C.
Cairo
Cairo is a 2D graphics library that provides high-quality vector-based drawing capabilities for multiple output devices and backends.
-
D.
Cairo
Cairo is a 2D graphics library that provides high-quality vector-based drawing with support for multiple output backends such as image buffers, PDF, and SVG.
-
E.
Alexandria, Egypt
Alexandria, Egypt is a historic Mediterranean port city founded by Alexander the Great, renowned for its ancient library, lighthouse, and enduring cultural significance in Egypt.
- 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_69e245489dd88190b1f674acf61c8769 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1765d10588190b4574f3e64617cd4 |
completed | April 29, 2026, 3:09 a.m. |
Created at: April 17, 2026, 3:07 p.m.