Triple
T18840371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tonlé Sap Lake |
E460776
|
entity |
| Predicate | areaDrySeason |
P133122
|
FINISHED |
| Object | approximately 2,500–3,000 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 2,500–3,000 square kilometers | Statement: [Tonlé Sap Lake, areaDrySeason, approximately 2,500–3,000 square kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaDrySeason Context triple: [Tonlé Sap Lake, areaDrySeason, approximately 2,500–3,000 square kilometers]
-
A.
area21stCenturyDrySeason
Indicates the area associated with dry-season conditions during the 21st century.
-
B.
drySeason
Indicates that the relationship or action occurs during, or is characteristic of, a period with little or no rainfall.
-
C.
hasDrySeasonCause
Indicates that one factor or condition is the underlying cause of a location or region experiencing a dry season.
-
D.
growingSeason
Indicates the period of the year during which growth or development actively occurs for the referenced entity.
-
E.
droughtType
Indicates the specific category or classification of a drought affecting an area or system.
- 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.