Triple
T3483606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niger State |
E73553
|
entity |
| Predicate | hasTouristAttraction |
P530
|
FINISHED |
| Object | Gurara Falls |
E360240
|
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: Gurara Falls | Statement: [Niger State, hasTouristAttraction, Gurara Falls]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gurara Falls Context triple: [Niger State, hasTouristAttraction, Gurara Falls]
-
A.
Gurara Falls
chosen
Gurara Falls is a major scenic waterfall and popular tourist attraction in central Nigeria, renowned for its impressive cascades and natural beauty.
-
B.
Lugard Falls
Lugard Falls is a series of spectacular white-water rapids and eroded rock formations on the Galana River in Kenya, known for its dramatic scenery and wildlife viewing.
-
C.
Itanda Falls
Itanda Falls is a powerful series of white-water rapids on the Victoria Nile in Uganda, popular for rafting and kayaking.
-
D.
Ngonye Falls
Ngonye Falls is a broad, horseshoe-shaped waterfall on the Zambezi River in western Zambia, noted for its scenic beauty and less-developed, remote setting compared to Victoria Falls.
-
E.
Tanda Falls
Tanda Falls is a scenic waterfall and popular natural getaway located near Mirzapur in Uttar Pradesh, India.
- 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_69ad85b3c9b08190857cae74c7f36da9 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb781e9c8190810fdd814f506127 |
completed | March 8, 2026, 6:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b373b5086081908ee4491860597557 |
completed | March 13, 2026, 2:17 a.m. |
Created at: March 8, 2026, 3:17 p.m.