Triple
T34745560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peru–Bolivia border |
E1001625
|
entity |
| Predicate | hasLakeSection |
P199756
|
FINISHED |
| Object | border through Lake Titicaca between Peru and Bolivia |
—
|
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: border through Lake Titicaca between Peru and Bolivia | Statement: [Peru–Bolivia border, hasLakeSection, border through Lake Titicaca between Peru and Bolivia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLakeSection Context triple: [Peru–Bolivia border, hasLakeSection, border through Lake Titicaca between Peru and Bolivia]
-
A.
hasLakeLandscape
Indicates that an entity features or is characterized by a landscape that includes a lake.
-
B.
hasLakeRegion
Indicates that a place or geographic area includes or is associated with a specific lake region.
-
C.
hasLakeOrKund
Indicates that an entity possesses, contains, or is associated with a lake or kund (a water reservoir or pond).
-
D.
hasLakeOnCourse
Indicates that a course (such as a route or path) includes at least one lake situated along its way or within its boundaries.
-
E.
hasLakes
Indicates that one entity possesses, contains, or is characterized by the presence of one or more lakes.
- F. None of above. chosen
Provenance (4 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_69f76db0367081909b57c50a7fb03025 |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69ff53389a0481908b2baeb43c6294f0 |
completed | May 9, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69ff52e2b4b88190b38d160d771fe14b |
completed | May 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69ff5337e6f88190ae0418335477063c |
completed | May 9, 2026, 3:31 p.m. |
Created at: May 3, 2026, 3:59 p.m.