Triple
T16259934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sipiso-piso Waterfall |
E394726
|
entity |
| Predicate | viewpointFeature |
P61635
|
FINISHED |
| Object | panoramic views of Lake Toba |
—
|
LITERAL 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: panoramic views of Lake Toba | Statement: [Sipiso-piso Waterfall, viewpointFeature, panoramic views of Lake Toba]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: viewpointFeature Context triple: [Sipiso-piso Waterfall, viewpointFeature, panoramic views of Lake Toba]
-
A.
viewpointFrom
Indicates a relationship where something is observed, depicted, or described from the perspective or location of a particular entity or point.
-
B.
featureOfInterest
Indicates the entity or object that is the primary subject or focus of the described observation, measurement, or analysis.
-
C.
sceneFeature
chosen
Indicates a characteristic, element, or attribute that is present within or helps define a particular scene.
-
D.
featuresPerspective
Indicates that one entity presents, embodies, or incorporates the viewpoint, angle, or outlook provided by another entity.
-
E.
viewpointTown
Indicates that a particular town is the location from which something is viewed, described, or considered.
- F. None of above.
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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e245c3082c8190a1c9f92b255fdbb6 |
completed | April 17, 2026, 2:37 p.m. |
| PD | Predicate disambiguation | batch_69e219f259e88190bf49d8408c04178e |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:04 a.m.