Triple
T9211005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abdullah bin Abdulaziz Al Saud |
E221115
|
entity |
| Predicate | birthPlace |
P1
|
FINISHED |
| Object | Nejd |
E128143
|
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: Nejd | Statement: [Abdullah bin Abdulaziz Al Saud, birthPlace, Nejd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nejd Context triple: [Abdullah bin Abdulaziz Al Saud, birthPlace, Nejd]
-
A.
Nejd
chosen
Nejd is a vast central region of the Arabian Peninsula that historically served as the heartland of the Saudi state and the birthplace of its ruling dynasty.
-
B.
Naju
Naju is a historic city in South Korea known for its pear cultivation and location in the southwestern province of South Jeolla.
-
C.
Nied
Nied is a district of Frankfurt am Main in Germany, located along the River Main and known for its mix of residential areas and green spaces.
-
D.
Neka
Neka is a city in northern Iran known for its location near the Caspian Sea and its role as an industrial and agricultural center in Mazandaran Province.
-
E.
Némi
Némi is an Oceanic language spoken by a small indigenous community in New Caledonia.
- 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_69ca83e9d0e081908bdb71097201a06c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd9b54520819087030148dadd6385 |
completed | April 1, 2026, 8:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d065efcb64819097d4624bd9e423d2 |
completed | April 4, 2026, 1:14 a.m. |
Created at: March 30, 2026, 7:27 p.m.