Triple
T6260588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Libyan Desert |
E140285
|
entity |
| Predicate | hasRegion |
P285
|
FINISHED |
| Object | Rebiana Sand Sea |
E136947
|
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: Rebiana Sand Sea | Statement: [Libyan Desert, hasRegion, Rebiana Sand Sea]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rebiana Sand Sea Context triple: [Libyan Desert, hasRegion, Rebiana Sand Sea]
-
A.
Genipabu dunes
Genipabu dunes are a famous coastal sand dune and lagoon complex near Natal in northeastern Brazil, renowned for its dramatic landscapes, buggy rides, and recreational activities.
-
B.
Great Sand Sea
chosen
The Great Sand Sea is a vast expanse of towering sand dunes and hyper-arid desert located in the eastern Sahara between Egypt and Libya.
-
C.
Marie Desert
Marie Desert is a film professional credited as an assistant on the movie "Empire."
-
D.
Tengger Desert
The Tengger Desert is a vast arid region in north-central China known for its extensive sand dunes and harsh continental climate.
-
E.
Hunder sand dunes
Hunder sand dunes are a high-altitude cold desert landscape in Ladakh, India, famous for their stark sand formations and double-humped Bactrian camels.
- 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_69c008c95c5c819084bd3dd56133d84d |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063856f308190a351a661caaae5f9 |
completed | March 22, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5192b99d4819083ab6e6f2092547b |
completed | March 26, 2026, 11:31 a.m. |
Created at: March 22, 2026, 4:24 p.m.