Triple
T18840370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tonlé Sap Lake |
E460776
|
entity |
| Predicate | areaWetSeason |
P133121
|
FINISHED |
| Object | approximately 10,000–16,000 square kilometers |
—
|
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: approximately 10,000–16,000 square kilometers | Statement: [Tonlé Sap Lake, areaWetSeason, approximately 10,000–16,000 square kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaWetSeason Context triple: [Tonlé Sap Lake, areaWetSeason, approximately 10,000–16,000 square kilometers]
-
A.
area21stCenturyDrySeason
Indicates the area associated with dry-season conditions during the 21st century.
-
B.
wetSeasonAccessibility
Indicates how easily or reliably something can be reached, used, or traversed during the wet or rainy season.
-
C.
wetSeasonCapital
Indicates that a location serves as the capital or primary administrative center specifically during the wet season.
-
D.
watershedArea
Indicates the total land area from which surface water drains into a particular water body or point in the drainage system.
-
E.
areaWater
Indicates the relationship between a geographic entity and the total area of its surface that is covered by water.
- 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_69d8dcfa11e4819090ab1ef5bdcd2b2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5b8e8f57081909edbbcaf56189816 |
completed | April 20, 2026, 5:26 a.m. |
| PD | Predicate disambiguation | batch_69e48d1e7dac81909ea1e758c87773c5 |
completed | April 19, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e49785fd7081909577e90a55df0a35 |
completed | April 19, 2026, 8:51 a.m. |
Created at: April 10, 2026, 11:56 a.m.