Triple
T7372212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Graskop |
E170034
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Lisbon Falls |
E530034
|
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: Lisbon Falls | Statement: [Graskop, locatedNear, Lisbon Falls]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lisbon Falls Context triple: [Graskop, locatedNear, Lisbon Falls]
-
A.
Lisbon Falls
chosen
Lisbon Falls is a scenic waterfall in South Africa’s Mpumalanga province, renowned for its dramatic drop and lush surroundings along the popular Panorama Route.
-
B.
Slate Falls
Slate Falls is a small rural community located within the township of Addington Highlands in eastern Ontario, Canada.
-
C.
Florence Falls
Florence Falls is a picturesque twin waterfall and popular swimming spot set amid monsoon forest in Australia’s Litchfield National Park.
-
D.
Ripley Falls
Ripley Falls is a picturesque, steep cascade waterfall in New Hampshire’s White Mountains, popular with hikers visiting Crawford Notch.
-
E.
Lobe Falls
Lobe Falls is a scenic waterfall and popular natural attraction located in the South Region.
- 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_69c68a5bfaac81909ce7f001dfb70c76 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f18451d88190ad4a2674279bb703 |
completed | March 27, 2026, 9:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c802c711788190806987567dbc9942 |
completed | March 28, 2026, 4:33 p.m. |
Created at: March 27, 2026, 3:07 p.m.