Triple
T249672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kigoma Region |
E5115
|
entity |
| Predicate | lakeShoreLength |
P1568
|
FINISHED |
| Object | long shoreline on Lake Tanganyika |
—
|
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: long shoreline on Lake Tanganyika | Statement: [Kigoma Region, lakeShoreLength, long shoreline on Lake Tanganyika]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lakeShoreLength Context triple: [Kigoma Region, lakeShoreLength, long shoreline on Lake Tanganyika]
-
A.
shorelineLength
chosen
Indicates the total measured extent of a land area’s boundary where it meets a body of water.
-
B.
numberOfLakes
Indicates the quantity of lakes associated with a given entity.
-
C.
hasMajorLake
Indicates that a geographic region or area contains at least one significant lake within its boundaries.
-
D.
locatedOnWaterbody
Indicates that an entity is situated on or directly adjacent to a specified body of water.
-
E.
coastlineFeature
Indicates that a geographic entity is a specific type of feature located along or forming part of a coastline.
- 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_69a257c4bf688190a46ebbf411ab7473 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d3728f0819086214ccc2db2305a |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b665f8c8190aac6fcbba2a0eebb |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:54 a.m.