Triple
T18840374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tonlé Sap Lake |
E460776
|
entity |
| Predicate | averageDepthDrySeason |
P133124
|
FINISHED |
| Object | around 1 meter |
—
|
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: around 1 meter | Statement: [Tonlé Sap Lake, averageDepthDrySeason, around 1 meter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageDepthDrySeason Context triple: [Tonlé Sap Lake, averageDepthDrySeason, around 1 meter]
-
A.
drySeason
Indicates that the relationship or action occurs during, or is characteristic of, a period with little or no rainfall.
-
B.
area21stCenturyDrySeason
Indicates the area associated with dry-season conditions during the 21st century.
-
C.
hasDrySeasonCause
Indicates that one factor or condition is the underlying cause of a location or region experiencing a dry season.
-
D.
riverbedDepth
Indicates the depth or vertical distance from the water surface to the bottom of a river at a given location or time.
-
E.
droughtDurationYears
Indicates the number of years that a drought condition persists or has persisted.
- 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.